{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T01:42:01Z","timestamp":1766454121960,"version":"3.41.2"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":63,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Foundation of Suzhou Medical College of Soochow University","award":["MX13401423","MP13405423"],"award-info":[{"award-number":["MX13401423","MP13405423"]}]},{"name":"Suzhou City University; Medical and Health Science and Technology Innovation Project of Suzhou","award":["SKYD2022097","SKY2022010"],"award-info":[{"award-number":["SKYD2022097","SKY2022010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections. In this study, we presented a graph-driven framework named BioGSF for RE from the literature by integrating shortest dependency paths (SDP) with entity-pair graph through the employment of the graph neural network model. Initially, we leveraged dependency relationships to obtain the SDP between entities and incorporated this information into the entity-pair graph. Subsequently, the graph attention network was utilized to acquire the topological information of the entity-pair graph. Ultimately, the obtained topological information was combined with the semantic features of the contextual information for relation classification. Our method was evaluated on two distinct datasets, namely S4 and BioRED. The outcomes reveal that BioGSF not only attains the superior performance among previous models with a micro-F1 score of 96.68% (S4) and 96.03% (BioRED), but also demands the shortest running times. BioGSF emerges as an efficient framework for biomedical RE.<\/jats:p>","DOI":"10.1093\/bib\/bbaf025","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T12:01:44Z","timestamp":1737720104000},"source":"Crossref","is-referenced-by-count":3,"title":["BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction"],"prefix":"10.1093","volume":"26","author":[{"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"Computing Science and Artificial Intelligence College, Suzhou City University , No. 1188 Wuzhong Avenue, Wuzhong District Suzhou , Suzhou 215004 ,","place":["China"]},{"name":"Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare , No. 1188 Wuzhong Avenue, Wuzhong District Suzhou , Suzhou 215004 ,","place":["China"]},{"name":"School of Computer Science & Technology, Soochow University , No. 1 Shizi Street, Suzhou 215000 ,","place":["China"]}]},{"given":"Zixuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Soochow University , No. 1 Shizi Street, Suzhou 215000 ,","place":["China"]}]},{"given":"Yuyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Soochow University , No. 1 Shizi Street, Suzhou 215000 ,","place":["China"]}]},{"given":"Huifang","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Suzhou Medical College of Soochow University , No. 199 Renai Road, SIP, Suzhou 215123 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5016-575X","authenticated-orcid":false,"given":"Wenying","family":"Yan","sequence":"additional","affiliation":[{"name":"Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare , No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004 ,","place":["China"]},{"name":"School of Basic Medical Sciences, Suzhou Medical College of Soochow University , No. 199 Renai Road, SIP, Suzhou 215123 ,","place":["China"]},{"name":"Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University , No. 199 Renai Road, SIP, Suzhou 215123 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"2025012412013270200_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10465-9","article-title":"Knowledge graphs: Opportunities and challenges","author":"Peng","year":"2023","journal-title":"Artif Intell Rev"},{"key":"2025012412013270200_ref2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1093\/bib\/bbac543","article-title":"A comprehensive review on knowledge graphs for complex diseases","volume":"24","author":"Yang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025012412013270200_ref3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1093\/bioinformatics\/btae194","article-title":"NetMe 2.0: A web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph","volume":"40","author":"Di Maria","year":"2024","journal-title":"Bioinformatics"},{"key":"2025012412013270200_ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad080","article-title":"The scalable precision medicine open knowledge engine (SPOKE): A massive knowledge graph of biomedical information","volume":"39","author":"Morris","year":"2023","journal-title":"Bioinformatics"},{"key":"2025012412013270200_ref5","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1016\/j.csbj.2024.03.021","article-title":"MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer","volume":"23","author":"Yang","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"2025012412013270200_ref6","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1038\/s41467-023-39301-y","article-title":"Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers","volume":"14","author":"Bang","year":"2023","journal-title":"Nat Commun"},{"key":"2025012412013270200_ref7","doi-asserted-by":"publisher","first-page":"100655","DOI":"10.1016\/j.xgen.2024.100655","article-title":"Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics","volume":"4","author":"Ni","year":"2024","journal-title":"Cell Genomics"},{"key":"2025012412013270200_ref8","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202405395","article-title":"Development and validation of an AI-driven system for automatic literature analysis and molecular regulatory network construction, advanced","volume":"11","author":"Li","year":"2024","journal-title":"Science"},{"key":"2025012412013270200_ref9","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1038\/s41587-021-01145-6","article-title":"A knowledge graph to interpret clinical proteomics data","volume":"40","author":"Santos","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025012412013270200_ref10","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":"2025012412013270200_ref11","doi-asserted-by":"publisher","first-page":"101817","DOI":"10.1016\/j.artmed.2020.101817","article-title":"Real-world data medical knowledge graph: Construction and applications(MKG)","volume":"103","author":"Li","year":"2020","journal-title":"Artif Intell Med"},{"key":"2025012412013270200_ref12","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1093\/jamia\/ocw041","article-title":"Text mining for precision medicine: Automating disease-mutation relationship extraction from biomedical literature","volume":"23","author":"Singhal","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2025012412013270200_ref13","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2025012412013270200_ref14","doi-asserted-by":"publisher","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: Generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025012412013270200_ref15","doi-asserted-by":"publisher","first-page":"btae075","DOI":"10.1093\/bioinformatics\/btae075","article-title":"Genegpt: Augmenting large language models with domain tools for improved access to biomedical information","volume":"40","author":"Jin","year":"2024","journal-title":"Bioinformatics"},{"volume-title":"The 54th Annual Meeting of the Association for Computational Linguistics","year":"2016","author":"Miwa","key":"2025012412013270200_ref16"},{"key":"2025012412013270200_ref17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1162\/tacl_a_00049","article-title":"Cross-sentence N-ary relation extraction with graph LSTMs, transactions of the association for","volume":"5","author":"Peng","year":"2017","journal-title":"Comput Linguist"},{"key":"2025012412013270200_ref18","doi-asserted-by":"crossref","first-page":"5678","DOI":"10.1093\/bioinformatics\/btaa1087","article-title":"BERT-GT: Cross-sentence N-ary relation extraction with BERT and graph transformer","volume":"36","author":"Lai","year":"2020","journal-title":"Bioinformatics"},{"key":"2025012412013270200_ref19","first-page":"4458","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","author":"Tian","year":"2021"},{"key":"2025012412013270200_ref20","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.18653\/v1\/2021.findings-acl.221","article-title":"Relation extraction with type-aware map memories of word dependencies","volume":"2021","author":"Chen","year":"2021","journal-title":"In: Findings of the Association for Computational Linguistics: ACL-IJCNLP"},{"key":"2025012412013270200_ref21","doi-asserted-by":"publisher","first-page":"bbac282","DOI":"10.1093\/bib\/bbac282","article-title":"BioRED: A rich biomedical relation extraction dataset","volume":"23","author":"Luo","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025012412013270200_ref22","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baw068","article-title":"BioCreative V CDR task corpus: A resource for chemical disease relation extraction","volume":"2016","author":"Li","year":"2016","journal-title":"Database"},{"key":"2025012412013270200_ref23","first-page":"141","volume-title":"Proceedings of the Sixth BioCreative Challenge Evaluation Workshop","author":"Krallinger","year":"2017"},{"key":"2025012412013270200_ref24","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baad080"},{"key":"2025012412013270200_ref25","first-page":"1","volume-title":"Proceedings of the 4th Learning Language in Logic Workshop (LLL05)","author":"N\u00e9dellec","year":"2005"},{"key":"2025012412013270200_ref26","first-page":"1","volume-title":"BMC Bioinformatics","author":"Pyysalo","year":"2008"},{"key":"2025012412013270200_ref27","first-page":"319","volume-title":"18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019","author":"Neumann","year":"2019"},{"key":"2025012412013270200_ref28","doi-asserted-by":"crossref","first-page":"i180","DOI":"10.1093\/bioinformatics\/btg1023","article-title":"GENIA corpus - a semantically annotated corpus for bio-textmining","volume":"19","author":"Kim","year":"2003","journal-title":"Bioinformatics"},{"key":"2025012412013270200_ref29","first-page":"57","article-title":"OntoNotes: The 90% solution","volume":"Companion Volume: Short Papers","author":"Hovy","year":"2006","journal-title":"Proceedings of the Human Language Technology Conference of the NAACL"},{"key":"2025012412013270200_ref30","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/978-94-024-0881-2_20","volume-title":"Handbook of Linguistic Annotation","author":"Pradhan","year":"2017"},{"key":"2025012412013270200_ref31","first-page":"97","volume-title":"ACL","author":"Murty","year":"2018"},{"key":"2025012412013270200_ref32","first-page":"1156","volume-title":"Asian Conference on Machine Learning","author":"Zhao","year":"2019"},{"key":"2025012412013270200_ref33","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1145\/3357384.3358119","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Wu","year":"2019"},{"key":"2025012412013270200_ref34","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1186\/s12859-023-05601-9","article-title":"BioEGRE: A linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network","volume":"24","author":"Zheng","year":"2023","journal-title":"BMC bioinformatics"},{"article-title":"GPT-4 technical report","key":"2025012412013270200_ref35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.08774"},{"volume-title":"The Llama 3 Herd of Models","key":"2025012412013270200_ref36","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2407.21783"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbaf025\/61619277\/bbaf025.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbaf025\/61619277\/bbaf025.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T12:01:53Z","timestamp":1737720113000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf025\/7978671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf025","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbaf025"}}