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LLMs and Biomedical Literature Annotation

Edited by:

Jin-Dong Kim, PhD, Database Center for Life Science - Research Organization of Information and Systems, Japan
Lars Juhl Jensen, PhD, University of Copenhagen, Denmark
Zhiyong Lu, PhD, National Library of Medicine - National Institutes of Health, United States of America
Fabio Rinaldi, PhD, Dalle Molle Institute for Artificial Intelligence - University of Italian Switzerland, Switzerland

Submission Status: Closed

This collection is no longer accepting submissions.

Genomics & Informatics is calling for submissions to our Collection on Large Language Models (LLMs) and Biomedical Literature Annotation.

Image credit: © natali_mis /

About the Collection

The field of biomedical literature mining is increasingly vital, facilitating the extraction of embedded knowledge from the extensive corpus of biomedical texts through automated, high-throughput techniques. The integration of Large Language Models (LLMs) has further highlighted its importance, showcasing significant capabilities in processing natural language.

BLAH (Biomedical Linked Annotation Hackathon) is an established series of annual hackathons dedicated to promoting open collaboration in the areas of biomedical literature annotation and mining. BLAH aims to enhance the interoperability among diverse linguistic resources, such as corpora, terminologies, ontologies, annotation datasets, and language models. This initiative is designed to create a unified and efficient ecosystem for all stakeholders in the field.

The 8th edition of BLAH, held in January 2024, embraced a pivotal theme: "Biomedical Annotations in the Age of LLMs." This theme not only reflects a contemporary focus within the community but also demonstrates a commitment to remain at the cutting edge of technological progress in the biomedical domain. The participants explored the dynamic interplay between LLMs and literature annotations, delving into a range of biomedical applications.

This special issue, titled ‘LLMs and Biomedical Literature Annotation,’ seeks to compile papers that present findings and insights garnered from BLAH8. Contributions from participants are warmly invited.

Meet the Guest Editors

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Jin-Dong Kim, PhD, Database Center for Life Science - Research Organization of Information and Systems, Japan

Dr. Kim serves as an Project Associate Professor at the Database Center for Life Science (DBCLS) within the Research Organization of Information and Systems (ROIS). His work focuses on natural language processing (NLP) and text mining, specifically tailored to the life sciences. Previously, he held the position of project lecturer at the University of Tokyo until 2010. Dr. Kim has authored over 100 peer-reviewed scientific papers, accumulating more than 7,000 citations. His efforts are directed towards improving the accessibility and interoperability of text mining resources in the life sciences through long-term initiatives such as PubAnnotation and PubDictionaries. Additionally, he has been instrumental in organizing key events including the Biomedical Linked Annotation Hackathon (BLAH) series and the BioNLP Open Shared Task (BioNLP-OST) series.

Lars Juhl Jensen, PhD, University of Copenhagen, Denmark

 Dr. Jensen started his research career in Søren Brunak’s group at the Technical University of Denmark, from where he in 2002 received the Ph.D. degree in bioinformatics, having worked on methods for protein function prediction, visualization of microbial genomes, pattern recognition in promoter regions, and microarray analysis. From 2003 to 2008, he was at the European Molecular Biology Laboratory working on text mining, integration of omics data, and network analysis. Since 2009, he has continued this line of research as a professor at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen and as a founder and scientific advisor of Intomics, now ZS Discovery. He has authored and co-authored more than 200 scientific publications that have received over 100,000 citations in total. He was awarded the Lundbeck Foundation Talent Prize in 2003, “Break-through of the Year” in 2006 by the magazine Ingeniøren, and the Lundbeck Foundation Prize for Young Scientists in 2010.

Zhiyong Lu, PhD, National Library of Medicine - National Institutes of Health, United States of America

Dr. Zhiyong Lu is a tenured Senior Investigator at the NIH/NLM IPR, leading research in biomedical text and image processing, information retrieval, and AI/machine learning. In his role as Deputy Director for Literature Search at NCBI, Dr. Lu oversees the overall R&D efforts to improve literature search and information access in resources like PubMed and LitCovid, which are used by millions worldwide each day. Additionally, Dr. Lu is Adjunct Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC). Dr. Lu serves as an Associate Editor of Bioinformatics, Organizer of the BioCreative NLP challenge, and Chair of the ISCB Text Mining COSI. With over 350 peer-reviewed publications, Dr. Lu is a highly cited author, and a Fellow of the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

Fabio Rinaldi, PhD, Dalle Molle Institute for Artificial Intelligence - University of Italian Switzerland, Switzerland

Dr. Rinaldi is responsible for NLP research at IDSIA (Dalle Molle Institute for Artificial Intelligence), in Southern Switzerland, and a group leader at the Swiss Institute of Bioinformatics. Until 2019 he was a lecturer and senior researcher at the University of Zurich, as well as PI in a number of research projects. Dr. Rinaldi co-authored more than 100 scientific publications (including more than 40 journal papers), dealing with topics such as Ontologies, Entity Extraction, Answer Extraction, Text Classification, Document and Knowledge Management, Language Resources and Terminology.

Submission Guidelines

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This Collection welcomes submission of research and review articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Snapp. Please, select the appropriate Collection title “LLMs and Biomedical Literature Annotation" under the “Details” tab during the submission stage.

Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.