{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T13:12:32Z","timestamp":1784121152451,"version":"3.55.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The biomedical literature is the go-to source of information regarding relationships between biological entities, including genes, diseases, cell types, and drugs, but the rapid pace of publication makes an exhaustive manual exploration impossible. In order to efficiently explore an up-to-date repository of millions of abstracts, we constructed an efficient and modular natural language processing pipeline and applied it to the entire PubMed abstract corpora.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed SciLinker using open-source libraries and pre-trained named entity recognition models to identify human genes, diseases, cell types and drugs, normalizing these biological entities to the Unified Medical Language System (UMLS). We implemented a scoring schema to quantify the statistical significance of entity co-occurrences and applied a fine-tuned PubMedBERT model for gene-disease relationship extraction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified and analyzed over 30 million association sentences, including more than 11 million gene-disease co-occurrence sentences, revealing more than 1.25 million unique gene-disease associations. We demonstrate SciLinker\u2019s ability to extract specific gene-disease relationships using osteoporosis as a case study. We show how such an analysis benefits target identification as clinically validated targets are enriched in SciLinker-derived disease-associated genes. Moreover, this co-occurrence data can be used to construct disease-specific networks, providing insights into significant relationships among biological entities from scientific literature.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>SciLinker represents a novel text mining approach that extracts and quantifies associations between biomedical entities through co-occurrence analysis and relationship extraction from PubMed abstracts. Its modular design enables expansion to additional entities and text corpora, making it a versatile tool for transforming unstructured biomedical data into actionable insights for drug discovery.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2025.1528562","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:08:07Z","timestamp":1742368087000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SciLinker: a large-scale text mining framework for mapping associations among biological entities"],"prefix":"10.3389","volume":"8","author":[{"given":"Dongyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cora","family":"Ames","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shameer","family":"Khader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franck","family":"Rapaport","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Series B"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-1-0716-2305-3_12","article-title":"BioBERT and similar approaches for relation extraction","volume":"2496","author":"Bhasuran","year":"2022","journal-title":"Methods Mol. Biol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e0200699","DOI":"10.1371\/journal.pone.0200699","article-title":"Automatic extraction of gene-disease associations from literature using joint ensemble learning","volume":"13","author":"Bhasuran","year":"2018","journal-title":"PLoS One"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"267D","DOI":"10.1093\/nar\/gkh061","article-title":"The unified medical language system (UMLS): integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/s41586-019-1879-7","article-title":"A brief history of human disease genetics","volume":"577","author":"Claussnitzer","year":"2020","journal-title":"Nature"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e0179488","DOI":"10.1371\/journal.pone.0179488","article-title":"A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations","volume":"12","author":"Eftimov","year":"2017","journal-title":"PLoS One"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/1758-2946-6-17","article-title":"Chemical named entities recognition: a review on approaches and applications","volume":"6","author":"Eltyeb","year":"2014","journal-title":"J. Chem."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/frai.2020.00004","article-title":"An introductory review of deep learning for prediction models with big data","volume":"3","author":"Emmert-Streib","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref9","article-title":"Bioformer: an efficient transformer language model for biomedical text mining","author":"Fang","year":"2023"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"btac757","DOI":"10.1093\/bioinformatics\/btac757","article-title":"GSEApy: a comprehensive package for performing gene set enrichment analysis in Python","volume":"39","author":"Fang","year":"2023","journal-title":"Bioinformatics"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1186\/1471-2105-11-85","article-title":"LINNAEUS: a species name identification system for biomedical literature","volume":"11","author":"Gerner","year":"2010","journal-title":"BMC Bioinformat."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"baac019","DOI":"10.1093\/database\/baac019","article-title":"Diseases 2.0: a weekly updated database of disease-gene associations from text mining and data integration","volume":"2022","author":"Grissa","year":"2022","journal-title":"Database"},{"key":"ref13","author":"Gu","year":"2020"},{"key":"ref14","volume-title":"spaCy: Industrial-strength natural language processing in Python","author":"Honnibal","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.13189\/csit.2016.040101","article-title":"Discovery of gene-disease associations from biomedical texts","volume":"4","author":"Hou","year":"2016","journal-title":"Comput. Sci. Inf. Technol."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/nrg1768","article-title":"Literature mining for the biologist: from information retrieval to biological discovery","volume":"7","author":"Jensen","year":"2006","journal-title":"Nat. Rev. Genet."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"73729","DOI":"10.1109\/ACCESS.2019.2920708","article-title":"A neural named entity recognition and multi-type normalization tool for biomedical text mining","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep40154","article-title":"An analysis of disease-gene relationship from Medline abstracts by DigSee","volume":"7","author":"Kim","year":"2017","journal-title":"Sci. Rep."},{"key":"ref19","first-page":"188","article-title":"End-to-end neural conference resolution","author":"Lee","year":"2017"},{"key":"ref9001","doi-asserted-by":"publisher","first-page":"868015","DOI":"10.3389\/fgene.2022.868015","article-title":"StarGazer: a hybrid intelligence platform for drug target prioritization and digital drug repositioning using streamlit","volume":"13","author":"Lee","year":"2022","journal-title":"Front Genet."},{"key":"ref9003","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1186\/s12864-024-10971-2","article-title":"Leveraging large-scale multi-omics evidences to identify therapeutic targets from genome-wide association studies","volume":"25","author":"Lessard","year":"2024","journal-title":"BMC Genomics."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.2174\/0929867327666200330142432","article-title":"Osteoporosis: mechanism, molecular target and current status on drug development","volume":"28","author":"Li","year":"2021","journal-title":"Curr. Med. Chem."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e0140549","DOI":"10.1371\/journal.pone.0140549","article-title":"Association study between the FTCDNL1 (FONG) and susceptibility to osteoporosis","volume":"10","author":"Lu","year":"2015","journal-title":"PLoS One"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"e0152725","DOI":"10.1371\/journal.pone.0152725","article-title":"DiMeX: a text mining system for mutation-disease association extraction","volume":"11","author":"Mahmood","year":"2016","journal-title":"PLoS One"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"100756","DOI":"10.1016\/j.websem.2022.100756","article-title":"Comparison of biomedical relationship extraction methods and models for knowledge graph creation","volume":"75","author":"Milo\u0161evi\u0107","year":"2023","journal-title":"J. Web Semant."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/nrd.2017.244","article-title":"Impact of a five-dimensional framework on R&D productivity at AstraZeneca","volume":"17","author":"Morgan","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/bioinformatics\/btt677","article-title":"Protein-driven inference of miRNA\u2013disease associations","volume":"30","author":"M\u00f8rk","year":"2014","journal-title":"Bioinformatics"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1093\/bib\/bbw112","article-title":"A review of connectivity map and computational approaches in pharmacogenomics","volume":"19","author":"Musa","year":"2018","journal-title":"Brief. Bioinform."},{"key":"ref27","doi-asserted-by":"crossref","first-page":"319","DOI":"10.18653\/v1\/W19-5034","article-title":"ScispaCy: fast and robust models for biomedical natural language processing","volume-title":"Proceedings of the 18th BioNLP Workshop and Shared Task","author":"Neumann","year":"2019"},{"key":"ref28","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1126\/science.abj6987","article-title":"The complete sequence of a human genome","volume":"376","author":"Nurk","year":"2022","journal-title":"Science"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"e65390","DOI":"10.1371\/journal.pone.0065390","article-title":"The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text","volume":"8","author":"Pafilis","year":"2013","journal-title":"PLoS One"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ymeth.2014.11.020","article-title":"DISEASES: text mining and data integration of disease-gene associations","volume":"74","author":"Pletscher-Frankild","year":"2015","journal-title":"Methods"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"101","DOI":"10.18653\/v1\/2020.acl-demos.14","article-title":"Stanza: A python natural language processing toolkit for many human languages","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations","author":"Qi","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1186\/1471-2105-12-S2-S1","article-title":"A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents","volume":"12","author":"Segura-Bedmar","year":"2011","journal-title":"BMC Bioinformat."},{"key":"ref9002","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1093\/bib\/bbv118","article-title":"Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams","volume":"18","author":"Shameer","year":"2017","journal-title":"Brief Bioinform."},{"key":"ref34","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-10-1503-8_7","article-title":"Text mining for precision medicine: bringing structure to EHRs and biomedical literature to understand genes and health","volume-title":"Translational biomedical informatics. Advances in experimental medicine and biology","author":"Simmons","year":"2016"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.3390\/cells11081261","article-title":"GBA variants and Parkinson disease: mechanisms and treatments","volume":"11","author":"Smith","year":"2022","journal-title":"Cells"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.jbi.2015.08.008","article-title":"PKDE4J: entity and relation extraction for public knowledge discovery","volume":"57","author":"Song","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.14569\/IJACSA.2017.081220","article-title":"Bio-NER: biomedical named entity recognition using rule-based and statistical learners","volume":"8","author":"Soomro","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"4837","DOI":"10.1093\/bioinformatics\/btac598","article-title":"BERN2: an advanced neural biomedical named entity recognition and normalization tool","volume":"38","author":"Sung","year":"2022","journal-title":"Bioinformatics"},{"key":"ref40","first-page":"183","article-title":"Investigating domain-specific information for neural conference resolution on biomedical texts","author":"Trieu","year":"2018"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/s41573-023-00688-4","article-title":"Applications of single-cell RNA sequencing in drug discovery and development","volume":"22","author":"Van de Sande","year":"2023","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"W587","DOI":"10.1093\/nar\/gkz389","article-title":"PubTator central: automated concept annotation for biomedical full text articles","volume":"47","author":"Wei","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e38460","DOI":"10.1371\/journal.pone.0038460","article-title":"SR4GN: a species recognition software tool for gene normalization","volume":"7","author":"Wei","year":"2012","journal-title":"PLoS One"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"e0191568","DOI":"10.1371\/journal.pone.0191568","article-title":"Systematic identification of latent disease-gene associations from PubMed articles","volume":"13","author":"Zhang","year":"2018","journal-title":"PLoS One"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.1093\/jamia\/ocab090","article-title":"Biomedical and clinical English model packages for the stanza Python NLP library","volume":"28","author":"Zhang","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.1093\/bioinformatics\/btw358","article-title":"A knowledge-based approach for predicting gene\u2013disease associations","volume":"32","author":"Zhou","year":"2016","journal-title":"Bioinformatics"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1093\/bioinformatics\/btx815","article-title":"Gram-CNN: a deep learning approach with local context for named entity recognition in biomedical text","volume":"34","author":"Zhu","year":"2017","journal-title":"Bioinformatics"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1528562\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:08:22Z","timestamp":1743077302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1528562\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":50,"alternative-id":["10.3389\/frai.2025.1528562"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1528562","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,19]]},"article-number":"1528562"}}