Talented musician Cian Chantrill is being mourned across Bishop’s Stortford this week after his sudden death aged just 20. The popular barman at the Castle and Bridge House pubs passed away at university in Manchester,

Cian Chantrill: Family mourn hugely popular student music producer, performer, pub barman and Coventry City fan found dead at university in Manchester aged just 20

Stillwater, Okla.; and Cleveland, Miss. This list includes the Valencia, Spain, campus of Boston's renowned Berklee College of Music, the United Kingdom's famed BRIT School outside London, LIPA and the BIMM Institute, the largest provider of contemporary ...

Billboard's 2023 Top Music Business Schools

Conor McGregor is a MMA fighter and former world UFC world champion. His biggest bouts so far have come against the likes of Khabib Nurmagomedov, Nate Diaz and a boxing match with Floyd Mayweather Jr. Pictured ©News Group Newspapers Limited in England No ...

Conor McGregor

The BIMM consists of a group of independent colleges with over 7,000 students across Brighton, Birmingham, Bristol, Manchester, London, Dublin, Berlin, and Hamburg. IQ Digital House worked with ...

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Wednesday 4th October was certainly a hell of a day for young Brighton based punk quartet Noah And The Loners, as not only did they drop their brand new single titled ‘Hell Of A Day’, but they got to play a single launch gig at The Brunswick Basement Bar as well.

‘Hell Of A Day’ for Brighton based punks Noah And The Loners

Born and educated in Dublin, Conor O’Donohoe went to music college at BIMM but left after two weeks to become a full-time musician. He was in bands before co-founding Wild Youth, who represented Ireland in Eurovision 2023.

Wild Youth’s Conor O’Donohoe: ‘I think it’s in everyone’s best interests that I put my Eurovision gold jumpsuit in the bin’

HENRY Dorrell, an enthusiastic young musician from Harvington has been selected to receive the first Olly Irwin Award.

Harvington musician honoured with first Olly Irwin Award

“I found accents here hard to understand and that meant conversation couldn’t be very spontaneous with Irish people. I felt inferior or stupid at first because of that barrier.” But the more they warmed up to the accent, and to Irish culture, “now I get along very well with Irish people and also have a big group of friends from here”.

‘I found accents here hard to understand. I felt inferior or stupid at first because of that barrier’

Ahead of their eagerly anticipated appearance with the RTÉ Concert Orchestra as part of RTÉ's Culture Night celebrations, Gabriel P. Blake of Donegal sensations The Murder Capital explains what makes this particular homecoming gig one to remember.

The Murder Capital on coming home to Donegal for Culture Night

BIMM

bimm

On February 20, 2025, luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024, with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024. Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Electrification and Affordability Driven by a mixture of EU CO₂ emission standards and Germany’s own desire for increased EV usage, Germany has emerged as the EU’s largest market for EV technology, with domestic luxury vehicle manufacturers competing with Chinese EVs for market share. So far, Chinese EVs have been able to make a sizable dent in the German EV market, with nearly 44,000 Chinese EVs being sold in Germany in 2024, comprising 11.5% of the total 381,000 EVs registered last year, with Chinese EVs comprising over 40% of EV import market share in Germany in Q1 2024. Price competition has enabled Chinese-manufactured EVs to dominate not only Germany’s domestic market but also the world’s. Vehicles like Nio’s ET9, set to compete directly with BMW’s 7-series and Audi’s A8, and Xiaomi’s SU7 Ultra, competing with Porsche's Taycan Turbo S, compete directly with German luxury manufacturers at a fraction of the cost. Other Chinese EVs also compete head-to-head with German auto manufacturers, to the point where Chinese-made EVs’ affordability resulted in an EU-led anti-subsidy investigation in late 2023. Price competition has drastically affected German manufacturers, to the point where German auto supplier Bosch, the world’s largest auto supplier, made significant cuts to its workforce in 2024. Consumer preference, driven primarily by cost and affordability, has taken hold of the German domestic market, with a survey from 2024 revealing that almost two-thirds of Germans can now imagine purchasing a car from a Chinese manufacturer. In fact, this number among survey respondents shot up to 80% if the respondent was specifically interested in an EV, highlighting lower prices as a primary consumer motivator for domestic German consumers. Seamless Technology Integration as a Consumer Priority Technology integration in Chinese EVs has become a strong selling point for consumers. China, which has become a major, if not the largest, market for German luxury vehicles, has drawn the ire of Chinese consumers due to its hesitancy to integrate technology into its vehicles. In contrast, Chinese EVs, with features including advanced heads-up displays and information systems, enable a more pleasant driving experience. The utilization of connected Internet of Things (IoT) and cellular technologies to control everything within the vehicle’s purview and extenuating smart devices throughout the home and the workplace have resulted in China’s leadership in global EV manufacturing. These incorporations of software and convenience have been a primary factor that has resulted in a decrease in German automakers’ share of China’s market, controlling just 15% in the present day (compared to 25% just five years ago). In fact, the combined electric market share of all German manufacturers, including Audi, BMW, Mercedes, and Porsche, was a mere five percent in 2024. Chinese EV manufacturers’ strength in technology integration has even resulted in the exportation of these technologies. This was particularly evident in Auto China 2024, which saw the announcement of Japanese car brands integrating Chinese technologies, with agreements between Nissan and Baidu, Toyota and Tencent, and Honda and Huawei dominating the conversation at the show. Loss of Brand Power and Reputation The reputational power, brand, and “made in Germany” tagline have also evolved in recent history. However, recent reports indicate that consumers are indexing less on these factors in favor of electrification, technology integration, and price, resulting in reputational loss for German luxury auto manufacturers. To this extent, German manufacturers’ reputation of complex, industrious, and over-engineered vehicles has resulted in a corresponding backlash against these companies, with many consumers pointing to decreased quality, and subsequently, loss of brand perception. The perceived strength of German-manufactured vehicles and parts has received substantial criticism in recent history, with countless auto enthusiasts on forums such as Reddit, Bimmerpost, and MBWorld discussing the increasing decline of German luxury automobiles. Inversely, the perception and reputation of Chinese EVs have increased dramatically in recent years, with Chinese EVs performing well in many side-by-side comparisons against other electric vehicles, including German luxury cars. This can be seen in the dominance of Chinese EVs around the world, with Chinese EVs comprising over 60% of worldwide EV sales in 2024. Conclusion All in all, consumers are increasingly drawn to Chinese EVs due to their lower prices, advanced technology features, and increasing brand prestige as well as awareness. This shift is particularly noticeable in the share of Chinese EVs in the EU’s largest EV market, Germany, as well as in China, where German luxury auto manufacturers were anticipating as their primary revenue source for the foreseeable future. As a result, Chinese EVs have reshaped the global automobile ecosystem and displaced German luxury car manufacturers. While the legacy prestige of German automobiles and their desirability remains, it is clear that consumers are increasingly drawn to Chinese EVs, particularly given their affordability, technology integrations, and rising luxury features. Image Credit: Matthias Balk (source) On February 20, 2025, luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024, with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024. On February 20, 2025 , luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024 , with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024 . February 20, 2025 February 20, 2025 2024 2024 2024 2024 Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Electrification and Affordability Electrification and Affordability Driven by a mixture of EU CO₂ emission standards and Germany’s own desire for increased EV usage, Germany has emerged as the EU’s largest market for EV technology, with domestic luxury vehicle manufacturers competing with Chinese EVs for market share. So far, Chinese EVs have been able to make a sizable dent in the German EV market, with nearly 44,000 Chinese EVs being sold in Germany in 2024, comprising 11.5% of the total 381,000 EVs registered last year, with Chinese EVs comprising over 40% of EV import market share in Germany in Q1 2024 . EU CO₂ emission standards EU CO₂ emission standards desire desire emerged emerged 44,000 44,000 381,000 381,000 Q1 2024 Q1 2024 Price competition has enabled Chinese-manufactured EVs to dominate not only Germany’s domestic market but also the world’s. Vehicles like Nio’s ET9, set to compete directly with BMW’s 7-series and Audi’s A8, and Xiaomi’s SU7 Ultra, competing with Porsche's Taycan Turbo S, compete directly with German luxury manufacturers at a fraction of the cost. compete compete competing competing Other Chinese EVs also compete head-to-head with German auto manufacturers, to the point where Chinese-made EVs’ affordability resulted in an EU-led anti-subsidy investigation in late 2023 . Price competition has drastically affected German manufacturers, to the point where late 2023 late 2023 German auto supplier Bosch, the world’s largest auto supplier, made significant cuts to its workforce in 2024 . 2024 2024 Consumer preference, driven primarily by cost and affordability, has taken hold of the German domestic market, with a survey from 2024 revealing that almost two-thirds of Germans can now imagine purchasing a car from a Chinese manufacturer. In fact, this number among survey respondents shot up to 80% if the respondent was specifically interested in an EV, highlighting lower prices as a primary consumer motivator for domestic German consumers. 2024 2024 Seamless Technology Integration as a Consumer Priority Seamless Technology Integration as a Consumer Priority Technology integration in Chinese EVs has become a strong selling point for consumers. China, which has become a major, if not the largest, market for German luxury vehicles, has drawn the ire of Chinese consumers due to its hesitancy to integrate technology into its vehicles. In contrast, Chinese EVs, with features including advanced heads-up displays and information systems, enable a more pleasant driving experience. hesitancy hesitancy features features The utilization of connected Internet of Things (IoT) and cellular technologies to control everything within the vehicle’s purview and extenuating smart devices throughout the home and the workplace have resulted in China’s leadership in global EV manufacturing. These incorporations of software and convenience have been a primary factor that has resulted in a decrease in German automakers’ share of China’s market, controlling just 15% in the present day (compared to 25% just five years ago). In fact, the combined electric market share of all German manufacturers, including Audi, BMW, Mercedes, and Porsche, was a mere five percent in 2024 . decrease decrease 2024 2024 Chinese EV manufacturers’ strength in technology integration has even resulted in the exportation of these technologies. This was particularly evident in Auto China 2024, which saw the announcement of Japanese car brands integrating Chinese technologies, with agreements between Nissan and Baidu, Toyota and Tencent, and Honda and Huawei dominating the conversation at the show. announcement announcement Loss of Brand Power and Reputation The reputational power, brand, and “made in Germany” tagline have also evolved in recent history. However, recent reports indicate that consumers are indexing less on these factors in favor of electrification, technology integration, and price, resulting in reputational loss for German luxury auto manufacturers. recent reports recent reports To this extent, German manufacturers’ reputation of complex, industrious, and over-engineered vehicles has resulted in a corresponding backlash against these companies, with many consumers pointing to decreased quality, and subsequently, loss of brand perception. The perceived strength of German-manufactured vehicles and parts has received substantial criticism in recent history, with countless auto enthusiasts on forums such as Reddit , Bimmerpost , and MBWorld discussing the increasing decline of German luxury automobiles. pointing pointing Reddit Reddit Bimmerpost Bimmerpost MBWorld MBWorld Inversely, the perception and reputation of Chinese EVs have increased dramatically in recent years, with Chinese EVs performing well in many side-by-side comparisons against other electric vehicles, including German luxury cars. This can be seen in the dominance of Chinese EVs around the world, with Chinese EVs comprising over 60% of worldwide EV sales in 2024 . side-by-side comparisons side-by-side comparisons includ ing includ 2024 2024 Conclusion Conclusion All in all, consumers are increasingly drawn to Chinese EVs due to their lower prices, advanced technology features, and increasing brand prestige as well as awareness. This shift is particularly noticeable in the share of Chinese EVs in the EU’s largest EV market, Germany, as well as in China, where German luxury auto manufacturers were anticipating as their primary revenue source for the foreseeable future. As a result, Chinese EVs have reshaped the global automobile ecosystem and displaced German luxury car manufacturers. While the legacy prestige of German automobiles and their desirability remains, it is clear that consumers are increasingly drawn to Chinese EVs, particularly given their affordability, technology integrations, and rising luxury features. Image Credit: Matthias Balk ( source ) source

How China’s EVs and Their Integrated Technologies Are Displacing German Luxury Car Manufacturers

44,000 startups. 4,553 cities. “4 billion votes” It was such a blessing to get to know so many of your ideas and innovations from every corner of the world in 2021. The startup community amazed all of us with your hard work and persistence despite all of the challenges that we faced globally throughout last year. Not gonna lie, the competition got our hearts racing like Eurovision. We won’t let you wait for much longer, below are long-awaited results! ✨✨✨ To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series 🔥 Central Europe Central Europe has always caught our attention with the talented pool of people in tech community. But were they able to get through 2021 despite the pandemic? Let’s cheers to the most outstanding amongst 4,500+ startups who have been working hard this year: Basel, Switzerland An overwhelming vote for ~ in their mission of helping machinery companies drive more service revenues from their installed base MachiQ Software Berlin, Germany We have with a whooping 538 votes! Let’s support Plan A with its mission to help companies to measure, monitor and reduce their environmental footprint. Plan A Düsseldorf, Germany It doesn’t get any easier with Kicking off in helping people organize online schedules more easily. EasyWeek! Geneva, Switzerland Manage code quality? We have to give props to for their startup! SonarSource Hamburg, Germany Taking over Hamburg is , an acoustic device to reduce noises in vehicle cabins. No more annoying sounds on the road 🚗 ReCalm Köln, Germany Small business? Hoping to manage your marketing campaign? is winning the vote! rankingCoach Lausanne, Switzerland is a Swiss-based company focused on cryptocurrency management solutions built for financial institutions. Metaco Leipzig, Germany empowers companies to operate and utilize drones for the most effective industrial data applications. FlyNex Mainz, Germany is growing sustainable digital mobility startups! Next Mobility Labs Munich, Germany wins for top-line, high-security international Identity Management! ORY Stuttgart, Germany Remote desktop application? Ask anywhere. AnyDesk Vienna, Austria , an IT company enabling privacy-preserving big data applications in the digital age. Mostly AI Zug, Switzerland Way to go 1762 votes for the first provably live pure Proof-of-Stake (PoS) CBC-Casper blockchain designed for real-world applications. CasperLabs! Nyon Calling for robust data solutions for impact-driven organizations! Havuta LLC Zürich is revolutionizing small business lending to give easy access to capital to those who need it most: SMEs ✨ Teylor Heidelberg, Germany is an innovative HR startup providing a solution for fast and efficient recruiting of employees with simple job profiles. Talk'n'Job Stuttgart, Germany Congratulations for winning this title! AnyDesk Moving on to our buddies in... 🔥 Eastern Europe Eastern Europe is home to many familiar big names in our start-up world. Trace history for Miro, Telegram, and Grammarly and you know where they are from. Who is the next generation for Eastern European startups? Budapest, Hungary is a platform for mobile app development, enabling tens of thousands of developers to automate integration, testing, and deployment. Bitrise I__ __ stanbul, Turkey Leading here is - a brand new software intelligence platform- helps organizations deliver faster, high-quality products by connecting their DevOps tools. Oobeya Kraków, Poland is a human cloud platform that connects global enterprises with European software houses, offering tech talent as a service. Talent Alpha Kyiv, Ukraine 281 votes for , an AI powered app where people can swap faces in the videos, GIFs and images in just a few seconds. Reface Sofia, Bulgaria 387 votes for __ __378, leading Blockchain and DLT solutions company that specializes in helping enterprises and blockchain startups design, build and scale innovative solutions using Blockchain & DLT! LimeChain Warsaw, Poland is a next-generation blockchain document management software (DMS). DoxyChain Yerevan, Armenia Whoever clicked to vote for was sure a vote, because it ended up winning as a full-service software provider delivering Artificial Intelligence & Machine Learning solutions for businesses. SmartClick smart Almaty Just like the name suggested, provides one pass to dozens of gyms and studios. 1FIT Debrecen got all the coders ranked figured out! CodersRank Kharkiv is complete solution digital assets management! Pics.io Minsk, Belarus with 243 votes is the first blockchain-powered sports media platform. Scorum Moscow, Russian Federation 1 e-grocery company in Russia with 26 votes and #1 on our HackerNoon board for Minsk. SberMarket Saint Petersburg, Russian Federation leads this area as a personal finance application that rewards responsible spending and savings. CoinKeeper Kazan, Russian Federation Smelling a sweet victory? 3d App designers for flower bouquets is in the house! Flowito Western Europe 🔥 Brexit? Pandemic? It seems like startups in Western Europe still keep their spirits regardless. Startups in Western Europe that stood out in votes this year go to: Antwerp is on a streak! As a bug bounty platform, it ensures online security through a crowd-sourced platform for computer experts. intigriti Belfast A record-breaking 10,003 votes? is an independent coffee shop. Come grab a coffee! Bobs & Berts Birmingham What’s happening in Birmingham? We have , an innovative UK-based advanced lithium battery developer aiming to accelerate the global shift to cleaner. Aceleron Bordeaux 30 is an open-source web framework for building modern apps and websites with Vue.js. NuxtJS Boulogne-billancourt is a software company that provides an in-store analytics platform to optimize operations. DiGEiZ Brighton In for some jam? offers musical education services. BIMM Bristol Another tasty victory for Western Europe is enabling the strongest digital encryption on the internet. KETS Quantum Security Brussels A fashionable win for , a social fashion network. You scan barcodes in fashion shops and see every influencer wearing those outfits! Share your wishlist with your friends and earn cashback. SCANDALOOK Cardiff is a technology company developing end-to-end digital connectivity for the point-of-care diagnostics market. Bond Digital Health London A whooping number of 1085 vote__ __ offers hardware based cyber-security solution for authentication and encryption in a wired local network. Crypto Quantique Edinburgh Hopping up on the crypto movement, just launched last year but they are already rocking it at making crypto accessible to everyone. Zumo Ghent 5379 votes for ! A Belgian InsurTech startup that helps insurance providers all over the world to better connect with their digital customers. WeGroup Glasgow specializes in single-cell analysis transforming the life sciences. Biomage Paris Show some love in the city of love for , a sports platform that is connected to a mobile sports coaching application. AirFit Aberystwyth enables artificial intelligence, data science & digital innovation solutions for the agriculture, biotech & life sciences industries. Agxio Norwich Congratulations for the amazing win of 1124 votes! WATR Charleroi Congratulations for your amazing win! Hirering Gibraltar Give a round of applause to Fortknoxter! Waltham Abbey is ruling the area with its amazing 72 votes! RPAIR Antwerpen Rocking up Antwerpen area is the rising startup ! Willow Egham What’s happening in Egham? Proudly announcing winning the Noonies this year! ATA.ONE Leicester Leicester is calling the name of as the winner for the region! DeadHappy Macclesfield Who else but ? Congratulations on dominating the award! AeroCloud Northern Europe 🔥 Want to listen to some music? Spotify. Need some games to kill time? Candy Crush. What about a quick call to someone you haven’t seen in a long time? Skype. These are all startups that originated from Northern Europe! Noonies, let’s take a look at the upcoming generation of startups from Northern Europe that hopefully will see an even bigger and brighter future. Gothenburg, Sweden Shipping was never made for small shippers, so took on the mission to built an end-to-end logistics platform to empower small businesses to spend less time and money on shipping and more time on growing their businesses. Sendify The Netherlands provides a virtual platform for many kinds of conferences and events. Let's Get Digital Helsinki, Finland A curated collection of public basketball courts in the Helsinki Metropolitan Area called . Everyone is welcome to submit to their local court. Koripallopaikat Lund, Sweden develops an antibiotic-free, safe and effective treatment of vaginal fungal infections. Gedea Biotech Luxembourg, Luxembourg 🚀 is a job-matching platform that connects developers and tech experts with CTOs and team leads from the best tech companies across Europe. 👾 nexten.io Malmö, Sweden Feeling studious, Noonies? __ __e is a tool for Google Classroom, that enables formative matrix assessment towards course learning objectives directly. StudyBe Oslo, Norway is the platform supports a unified API across all connected device categories, removing significant friction for app developers. Domos Labs Reykjavík, Iceland __ __s is a mobile-first technologically advanced neo-bank. Indo service #S__ __ tockholm, Sweden is the biggest blockchain start-up in Sweden. Double winner of the most prestigious nomination in Nordics 'NyTeknik 33-listan', winner of the Deloitte Fast 50, and now Noonies Award! ChromaWay AB T__ __ allinn, Estonia Passing 1000 votes is a fully digital and decentralized ecosystem for all actors in supply chain. OBORTECH Vilnius, Lithuania is an easy-to-use money app for cards and accounts. Via Genome, users can open a business or a merchant account or a personal one with dedicated IBANs, access transfers, exchange currency, process payments, and manage the funds. Genome Aalborg Digura A legaltech offering tenants fast, digital and reliable legal assistance Aarhus Partisia Market Design provides secure, simple, efficient solutions for auctions and exchanges realizing the potential of the market. Helsingborg is a hi-tech company developing cardiac occlusion devices and other vascular implants. Occlutech Tallinna is an AI-powered system for marketing automation in social networks. POSTOPLAN 🔥 Southern Europe Southern Europe’s economy went through a hard time this year due to a huge reduction in tourism. But our startups are of course always headstrong, from Spain to Italy and all the way through Greece. Who are the heroes rising amidst changes of Southern Europe? Skopje is a versatile software development company that solves the business challenges of SMBs, big brands, and brave hearts with awesome ideas. CodeChem Sliema Sounds like just a relaxing exhale. is a holistic health and wellbeing app built to allow users work on the development of their physical, mental and spiritual health Few Tajonar is a company of experienced business & industry entrepreneurs offering solutions to industries based on cutting-edge technologies. das-Nano Tarragona is a technology-based company focusing on the production of integrated microsystems. IMICROQ Terni is an ICT-based integrated solution that is able to modulate, respond, assist and learn to control most home activities. Mind Tirana is a video content and e-commerce platform for the Balkans built on top of an Albanian language specialized search engine. Gjirafa Torino develops and commercializes algorithms based on Artificial Intelligence to increase the autonomy of small satellite missions. AIKO T__ __ orres Buy online groceries like you would buy offline with personalized picking as fast as in 4 hours with Vedras 360hyper! Trento is a music-based platform built around streaming platforms that allow users to discover new emerging artists and make new friends. Brots 2021, what a YEAR. Congratulations to all of the startups that made it to the top winning list for Europe! Stay tuned for Startup awards in 2022 and let’s cheers to a new year of more innovative ideas, bold innovations, and robust creations from you, Europe 🌷 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by An Le and edited by Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Europe

PALO ALTO, California, May 29th, 2025/CyberNewsWire/--Today, SquareX released new threat research on an advanced Browser-in-the-Middle (BitM) attack targeting Safari users. SquareX SquareX As highlighted by Mandiant, adversaries have been increasingly using BitM attacks to steal credentials and gain unauthorized access to enterprise SaaS apps. Mandiant Mandiant BitM attacks work by using a remote browser to trick victims into interacting with an attacker-controlled browser via a pop-up window in the victim’s browser. A common BitM attack involves displaying the legitimate login page of an enterprise SaaS app, deceiving victims into divulging credentials and other sensitive information thinking that they are conducting work on a regular browser window. Despite this, one flaw that BitM attacks always had was the fact that the parent window would still display the malicious URL, making the attack less convincing to a security-aware user. However, as part of the Year of Browser Bugs (YOBB) project, SquareX’s research team highlights a major Safari-specific implementation flaw using the Fullscreen API. Year of Browser Bugs Year of Browser Bugs SquareX SquareX When combined with BitM, this vulnerability can be exploited to create an extremely convincing Fullscreen BitM attack, where the BitM window opens up in fullscreen mode such that no suspicious URLs from the parent window is seen. Safari users are especially vulnerable to this attack as there is no clear visual indicator of users entering fullscreen. We have disclosed this vulnerability to Safari and were regrettably informed that there is no plan to address the issue. The current Fullscreen API specifies that “the user has to interact with the page or a UI element in order for this feature to work.” However, what the API does not specify is what kind of interaction is required to trigger fullscreen mode. Consequently, attackers can easily embed any button – such as a fake login button – in the pop-up that calls the Fullscreen API when clicked. This triggers a fullscreen BitM window that perfectly mimics a legitimate login page, including the URL displayed on the address bar. “The Fullscreen BitM attack highlights architectural and design flaws in browser APIs, specifically the Fullscreen API,” says the researchers at SquareX, “Users can unknowingly click on a fake button and trigger a fullscreen BitM window, especially in Safari where there is no notification when the user enters fullscreen mode. Users that typically rely on URLs to verify the legitimacy of a site will have zero visual cues that they are on an attacker-controlled site. With how advanced BitM is becoming, it is critical for enterprises to have browser-native security measures to stop attacks that can no longer be visually identified by even the most security aware individuals.” “The Fullscreen BitM attack highlights architectural and design flaws in browser APIs, specifically the Fullscreen API,” says the researchers at SquareX, “Users can unknowingly click on a fake button and trigger a fullscreen BitM window, especially in Safari where there is no notification when the user enters fullscreen mode. Users that typically rely on URLs to verify the legitimacy of a site will have zero visual cues that they are on an attacker-controlled site. With how advanced BitM is becoming, it is critical for enterprises to have browser-native security measures to stop attacks that can no longer be visually identified by even the most security aware individuals.” While BitM attacks have primarily been used to steal credentials, session tokens and SaaS application data, the fullscreen variant has the potential to lead to even more damage by making the attack imperceptible for most ordinary enterprise users. For instance, the landing site may have a button that claims to link to a government resource and opens up to a fake government advisory page to spread misinformation and even gather sensitive company and personally identifiable information (PII). The victim can even subsequently open additional tabs in the attacker-controlled window, allowing adversaries to fully monitor the victim’s browsing activity. Fullscreen BitM window displaying legitimate Figma login page and URL in the address bar (Disclaimer: Figma is used as an illustrative example) Are Other Browsers Vulnerable to Fullscreen BitM Attacks Too? Unlike Safari, Firefox, Chrome, Edge and other Chromium-based browsers display a user message whenever the full-screen mode is toggled. However, this notification is extremely subtle and momentary in nature - most employees may not notice or register this as a suspicious sign. Additionally, the attacker can also use dark modes and colors to make the notification even less noticeable. By contrast, Safari does not have a messaging requirement - the only visual sign of entering fullscreen mode is a “swipe” animation. Thus, while the attack shows no clear visual cues in Safari browsers, other browsers are also exposed to the same Fullscreen API vulnerability that makes the Fullscreen BitM attack possible. Existing security solutions fail to detect Fullscreen BitM attacks Unfortunately, EDRs have zero visibility into the browser and are proven to be obsolete when it comes to detecting any BitM attack, much less its more advanced fullscreen variant. Additionally, orchestrating the attack with technologies such as remote browser and pixel pushing will also allow it to bypass SASE/SSE detection by eliminating any suspicious local traffic. As a result, without access to rich browser metrics, it is impossible for security tools to detect and mitigate Fullscreen BitM attacks. Thus, as phishing attacks become more sophisticated to exploit architectural limitations of browser APIs that are either unfixable or will take significant time to fix by browser providers, it is critical for enterprises to rethink their defense strategy to include advanced attacks like Fullscreen BitM in the browser. To learn more about this security research, users can visit https://sqrx.com/fullscreen-bitm. https://sqrx.com/fullscreen-bitm https://sqrx.com/fullscreen-bitm SquareX’s research team is also holding a webinar on June 5th, 10am PT/1pm ET to dive deeper into the full attack chain. To register, users can click here. here here About SquareX SquareX is a pioneering Browser Detection and Response (BDR) that empowers organizations to proactively detect, mitigate, and effectively threat-hunt client-side web attacks. SquareX SquareX SquareX provides critical protection against a wide range of browser security threats, including malicious browser extensions, advanced spearphishing, browser-native ransomware, genAI DLP, and more. Unlike legacy security approaches and cumbersome enterprise browsers, SquareX seamlessly integrates with users' existing consumer browsers, ensuring enhanced security without compromising user experience or productivity. By delivering unparalleled visibility and control directly within the browser, SquareX enables security leaders to reduce their attack surface, gain actionable intelligence, and strengthen their enterprise cybersecurity posture against the newest threat vector - the browser. Users can find out more on www.sqrx.com. www.sqrx.com www.sqrx.com The Fullscreen BitM Attack disclosure is part of the Year of Browser Bugs project. Every month, SquareX’s research team releases a major web attack that focuses on architectural limitations of the browser and incumbent security solutions. Previously disclosed attacks include Browser Syncjacking, Polymorphic Extensions and Browser-Native Ransomware. Year of Browser Bugs Year of Browser Bugs Browser Syncjacking Browser Syncjacking Polymorphic Extensions Polymorphic Extensions Browser-Native Ransomware Browser-Native Ransomware To learn more about SquareX’s BDR, users can contact SquareX at founder@sqrx.com. For press enquiries on this disclosure or the Year of Browser Bugs, users can email at junice@sqrx.com. founder@sqrx.com founder@sqrx.com junice@sqrx.com junice@sqrx.com Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was published as a press release by Cybernewswire under HackerNoon’s Business Blogging Program. This story was published as a press release by Cybernewswire under HackerNoon’s Business Blogging Program. Program Program

Fullscreen BitM Attack Discovered By SquareX Exploits Browser Fullscreen APIs To Steal Credentials 

الهجوم BitM على الشاشة الكاملة الذي اكتشفه SquareX يستخدم API المتصفح على الشاشة الكاملة لسرقة الوثائق

“SquareX” tərəfindən aşkarlanan “Fullscreen BitM Attack” “Browser Fullscreen APIs”i istifadə edir

L'atac BitM de pantalla completa descobert per SquareX explota les APIs de navegador de pantalla completa per robar credencials

Útok BitM na plnou obrazovku, který objevil SquareX, využívá rozhraní pro prohlížení na plné obrazovce k krádeži pověření

Full-Screen-BitM-Angriff von SquareX entdeckt nutzt Browser-Full-Screen-APIs, um Anmeldeinformationen zu stehlen

Ataque BitM de pantalla completa descubierto por SquareX explota las APIs de navegador de pantalla completa para robar credenciales

Fullscreen BitM napad otkriveni od strane SquareX iskorištava preglednika punog ekrana API za krađu povjerljivih podataka

A SquareX által felfedezett teljes képernyős BitM támadás kihasználja a teljes képernyős böngésző API-kat a hitelesítő adatok ellopására

Serangan BitM Full Screen Ditemukan oleh SquareX Memanfaatkan API Browser Full Screen untuk Mencuri Credentials

SquareXが発見したフルスクリーンBitM攻撃は、ブラウザフルスクリーンAPIを利用して認証情報を盗む

Полноэкранная атака BitM, обнаруженная SquareX, использует браузерные полноэкранные API для кражи аккредитаций

Sulmi BitM me ekran të plotë i zbuluar nga SquareX shfrytëzon API-t e shfletuesit me ekran të plotë për të vjedhur identifikimet

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables Figure Legends and Tables This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

Figure Legends and Tables for Our Biomedical Text Mining Research

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 5.2. Limitations of Current Community Challenges There are some limitations in current community challenge evaluation tasks on biomedical text mining. Firstly, there is a lack of representativeness of data in evaluation tasks. Biomedical and healthcare data possess sensitivity and privacy concerns, making it difficult to obtain largescale datasets. This results in many tasks using small or synthetic datasets, which may limit the representativeness and applicability. Data quality and annotation are also challenging, as the annotation of medical data are complex and requires high levels of expertise from annotators. For data types, many tasks only collect a single type of data. In the future, task organizer should consider incorporating data from multiple sources and modalities. Additionally, some datasets are restricted for use only during the evaluations and not made available afterward, limiting the impact of community challenge evaluation tasks. Secondly, the developed solutions may lack sufficient innovation and exhibit poor reproducibility. Some participants tend to apply established methods to achieve quick results, rather than exploring novel and innovative approaches. This can lead to a lack of innovation and diversity in methods, limiting further technical advancements. On the other hand, some algorithms may perform well on specific datasets but fail to generalize to others, reducing their broad applicability and reliability. To encourage innovation, future community challenges should emphasize the exploration of novel techniques and reward participants for their creativity. When integrating with existing systems, algorithm performance must be evaluated across multiple datasets and scenarios to ensure robustness and generalizability. Lastly, there exists a gap between evaluation tasks and applications in clinical practice. These tasks are generally abstracted from complex real-world problems and simplified to facilitate evaluation and comparison. However, such simplification may not reflect the complexity, diversity, and ambiguity of problems in real-world clinical settings. For example, several tasks focus on fundamental NLP tasks, such as medical information extraction and text classification, while more intricate challenges closely aligned with clinical practice are lacking. In real-world clinical practice, models always need to handle more noise and uncertainty, which may not have been fully considered in current evaluation tasks. Therefore, even systems that perform well in evaluation tasks may not be able to achieve similar results in practical applications. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

Limitations of Current Biomedical Text Mining Community Challenges

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 5.1. Contributions of Community Challenges Community challenges has made significant contributions for biomedical text mining research in many aspects. We summarize five notable contributions ranging from research level to application level, including driving the development of data standards and evaluation benchmarks, promoting technological innovation and development, fostering interdisciplinary collaboration and communication, promoting education and training, facilitating translational informatics application (see Figure 4). Datasets and benchmarks Datasets and benchmarks The community challenges generally release datasets that are related to specific NLP tasks. These datasets are manually annotated by domain experts, and are sourced from a wide range of biomedical materials, such as published literatures, clinical records, and internet forums. These datasets serve as valuable resources for training and evaluating models, as well as benchmarks for comparing the performance of different approaches. For example, researchers have developed the benchmark CBLUE [64], which combines datasets from multiple shared tasks and aims to evaluate the model's ability in Chinese biomedical language understanding. Moreover, Recently, researchers have also transformed numerous tasks into prompt-based language generation tasks and construct an evaluation benchmark PromptCBLUE [59], which aims to evaluate the capabilities of large language models in healthcare domain. Technology innovation and development Technology innovation and development The community challenges encourage participants to propose state-of-the-art algorithms and methodologies to address problems in biomedical text mining. This competitive environment stimulates researchers' innovative thinking and technological advancements, driving the development and breakthroughs in the field. Taking the information extraction tasks as an example. Initially, early solutions relied on rule-based and dictionary-based methods. Then, deep neural network methods, particularly long short-term memory (LSTM) architecture, gradually gained traction and garnered significant attention. Subsequently, BERT renowned for their powerful text representation capabilities, were often used by participants and found applications in various biomedical fields [68]. In recent research, large language models such as ChatGPT have also been leveraged for purposes such as data augmentation or zero-shot information extraction [69], showcasing their potential for advancing the field even further. Interdisciplinary collaboration and communication Interdisciplinary collaboration and communication Numerous experts with different research background participate in these community challenges. These experts, hailing from both academia and industry, come together through academic conferences to showcase their algorithm architectures, performance outcomes, and explore potential collaboration opportunities. Additionally, the community challenges also provide benefits to participants through the publication of papers on specific task topics. This interdisciplinary collaboration and communication play a crucial role in promoting knowledge sharing and cooperation among different domains, thus driving the development of biomedical text mining. Education and training Education and training The evaluation tasks serve as valuable learning and practical resources for students and beginners in the BioNLP domain. They allow participants to focus on specific tasks, engage in competition, discuss with experts, and improve their skills by evaluating their methods and receiving constructive feedback. After the task is completed, the researchers can further utilize the dataset to evaluate the algorithms and publish articles. Moreover, these shared tasks also provide open access to relevant datasets, which are often used by universities for teaching courses and practical exercises to assess student learning performance. It is worth noting that some datasets explicitly state their restriction to be used only during community challenges and are not made available thereafter. Translational informatics application Translational informatics application Biomedical text mining has the potential to contribute significantly to the standardization of knowledge. By extracting valuable information from vast volumes of biomedical literature, it becomes possible to establish high-quality knowledge bases and ontologies. These resources, in turn, can offer timely evidence-based information for researchers, clinicians, and policy makers. For example, mining the gene-disease semantic associations from literatures can help researchers in discovering novel gene candidates related to diseases [70], while extracting PICOS information from the literature can contribute to the construction of clinical evidence knowledge bases [71]. Biomedical text mining plays a crucial role in clinical decision support. Through the extraction and normalization of clinical information, large quantities of unstructured clinical textual data can be converted into structured and computable formats. This transformation enables clinicians to access comprehensive and accurate patient information, facilitating clinical diagnosis and treatment recommendations. Moreover, biomedical text mining techniques can also be employed in epidemic trend prediction and risk assessment. By mining extensive clinical textual data, it becomes possible to identify key features and patterns associated with patients, which enabling the implementation of appropriate intervention measures to improve patient health outcomes. Biomedical text mining holds significant potential for commercial applications. In the community challenges, multiples evaluation tasks are organized by company. These tasks typically employ data from real-world healthcare sources and have clear clinical application scenarios, such as patient health consultation, and automated question answering. The successful development of solutions for these tasks can provide opportunities and competitive advantages for businesses. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

The Impact of Community Challenges on Biomedical Text Mining Research

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 4. Translational Informatics in Biomedical Text Mining Based on the perspective of translational informatics, Figure 3 demonstrates the community challenges in biomedical text mining, which aims in health information processing that collected from diverse biomedical resources by leveraging various NLP technologies, and has numerous applications in clinical translational research. Translational informatics refers to the application of data science and computational approaches to bridge the gap between biomedical research and clinical practice. In this context, biomedical text mining plays a crucial role in extracting valuable information from large volumes of biomedical texts, such as electronic health records, scientific literatures, clinical trial reports, medical books, clinical practice guidelines, and even the internet. By using a variety of NLP technologies, including named entity recognition, normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph, question answering, and text generation, biomedical text mining enables the efficient processing and analysis of biomedical texts, leading to various clinical translational applications such as medical text mining and information processing, clinical data management, terminology standardization, health consultation, question-answering, disease prediction, epidemic forecasting, clinical decisionmaking, and knowledge discovery. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

Translational Informatics & Biomedical Text Mining: Bridging Research to Practice

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 3.4 Text Generation and Knowledge Reasoning Text generation refers to the process of generate natural language text, while knowledge reasoning refers to the process of using existing knowledge and information to make inferences. Both of them are complex and challenging NLP tasks. In 2020, CHIP organized a question generation task focused on Chinese traditional medical literatures and related texts from internet forum. Participants were required to develop algorithms that process these texts and generate questions. In 2021, CCKS organized a dialogue generation task, which was based on the medical dialogue dataset MedDG [67] related to 12 types of common gastrointestinal diseases. The task aimed to generate question-answer pairs containing 160 related entities from five categories: diseases, symptoms, attributes, examinations, and medications. At same year, CCKS organized another task focused on the reading comprehension of Chinese popular medical knowledge. Given texts and questions, the task aims to extract corresponding text spans as answers. For medical knowledge reasoning, in 2021, CCL released the medical dialogue based intelligent diagnosis evaluation task, which explored the identification of medical entities and symptom information from doctor-patient dialogue texts, the automatic generation of medical reports, and the simulation of dialogue process to determine specific diseases. In 2022, CHIP organized a task, which aims to automatic clinical diagnostic coding by given relevant diagnostic information (e.g., admission diagnosis, preoperative diagnosis, postoperative diagnosis, and discharge diagnosis), surgery, medication, and medical advice [47]. 3.5 Large Language Model Evaluation Large language model (LLM) possesses powerful capability in text understanding and generation. Extensive research has explored wide range of potential applications for LLM. In biomedical and healthcare fields, higher evaluation standards are required for development and application of LLM due to its specialization, rigor, privacy, and ethical considerations. Ensuring the reliability and credibility of these models in clinical applications is crucial, making comprehensive evaluations of biomedical LLM extremely important. In 2023, based on the benchmark of CBLUE [64], CCKS organized a task which transformed various NLP tasks within different medical scenarios into prompt-based language generation tasks, creating a large-scale prompt tuning benchmark PromptCBLUE[59, 60]. Next, the organizers optimized the dataset and conducted evaluation tasks at CHIP in 2023 [50]. Additionally, CHIP organized another task in 2023, which released a dataset CHIP-YIER-LLM contains various multiple-choice questions collected from medical licensing exams, medical textbooks, medical literatures, clinical practice guidelines, publicly available EHRs. This task was designed to assess the capabilities of LLM in the field of biomedical research. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

Biomedical NLP: Text Generation & Knowledge Reasoning Tasks

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 3.3 Knowledge Graph and Question Answering The knowledge graph is a structured knowledge representation used to store and organize knowledge. It consists of a network of entities and their relationships. Knowledge graph have extensive applications in the field of biomedicine, it provides a way to organize and integrate diverse biomedical data from various sources, enabling researchers to explore complex relationships and make new discoveries. Biomedical knowledge question answering utilizes the biomedical knowledge graph and NLP techniques to enable computers to answer natural language questions related to biomedicine or healthcare, assisting doctors and researchers in more efficiently accessing and applying biomedical knowledge. In 2020, CCKS released the task of construction and question-answering of COVID-19 knowledge graph [56]. It defined four subtasks, including: 1) inference of entity types in the COVID-19 knowledge graph, 2) prediction of hierarchical relationships between entities, 3) link prediction, such as targeted effects of drugs and viruses or protein interactions, and 4) knowledge question answering: constructing question-answering data focused on specific subjects such as health, medicine, and disease prevention and control related to COVID-19. In 2021, CCKS released the task for link prediction in a multi-level knowledge graph of phenotypes, drugs, and molecules, aims to predict seven relationship categories [57]. In 2019, CCIR organized a task that provided a medical event graph built with EHRs, along with a series of natural language questions, requiring participants to build systems that can automatically return results for the given questions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

Biomedical Knowledge Graphs and Question Answering Systems

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 3.2 Text Classification and Text Similarity Text classification refers to the task of categorizing texts in the field of biomedical sciences based on their themes, types, or other characteristics. In 2019, CHIP introduced a clinical trial eligibility criteria text classification task, which aimed to classify criteria sentences into 44 predefined semantic categories [25, 61]. In 2021, CHIP released a task which aims to classify clinical descriptions collected from internet into negative and positive categories, based on their relevance to patient conditions. In 2023, CHIP released an internet diabetes consultation question classification task, which defined six classes, including diagnosis, treatment, common knowledge, healthy lifestyle, epidemiology, and other. In 2021, CSMI organized a task focused public health questions, which defined six classes, including diagnosis, treatment, anatomy/physiology, epidemiology, healthy lifestyle, and physician selection. It is worth noting that all of these tasks are single-label classification tasks. Text similarity refers to the measurement of the semantic or content-related similarity between two texts. For instance, in the tasks released by CHIP in 2018 and 2019, patient health consultation question corpora were collected from the internet, and given two questions, the objective was to determine if their intents were the same. On the other hand, in the task released by CCKS in 2021, focusing on medical popular science knowledge, questions and answers were provided, and the objective was to judge whether they matched or not [57]. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally;
(3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China;
(4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China;
(5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China;
(6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
(9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.

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