{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T11:17:28Z","timestamp":1778239048797,"version":"3.51.4"},"reference-count":193,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"am","delay-in-days":337,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000084","name":"National Science Foundation Directorate for Engineering","doi-asserted-by":"publisher","award":["2319449"],"award-info":[{"award-number":["2319449"]}],"id":[{"id":"10.13039\/100000084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000084","name":"National Science Foundation Directorate for Engineering","doi-asserted-by":"publisher","award":["2312502"],"award-info":[{"award-number":["2312502"]}],"id":[{"id":"10.13039\/100000084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017618","name":"National Institute of Diabetes and Digestive and Kidney Diseases Division of Diabetes Endocrinology and Metabolic Diseases","doi-asserted-by":"publisher","award":["K25DK135913"],"award-info":[{"award-number":["K25DK135913"]}],"id":[{"id":"10.13039\/100017618","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.jbi.2025.104861","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T23:28:04Z","timestamp":1753313284000},"page":"104861","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":23,"special_numbering":"C","title":["A review on knowledge graphs for healthcare: Resources, applications, and promises"],"prefix":"10.1016","volume":"169","author":[{"given":"Hejie","family":"Cui","sequence":"first","affiliation":[]},{"given":"Jiaying","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Ran","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Shiyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Shaojun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Kan","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhaohui S.","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Joyce C.","family":"Ho","sequence":"additional","affiliation":[]},{"given":"Tianfan","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Mengdi","family":"Huai","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9145-4531","authenticated-orcid":false,"given":"Carl","family":"Yang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.jbi.2025.104861_b1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge graphs: Representation, acquisition, and applications","volume":"33","author":"Ji","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.jbi.2025.104861_b2","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1016\/j.csbj.2020.05.017","article-title":"Constructing knowledge graphs and their biomedical applications","volume":"18","author":"Nicholson","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b3","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1162\/dint_a_00019","article-title":"Knowledge graph construction and applications for web search and beyond","volume":"1","author":"Wang","year":"2019","journal-title":"Data Intell."},{"key":"10.1016\/j.jbi.2025.104861_b4","doi-asserted-by":"crossref","unstructured":"X. Wang, X. He, Y. Cao, M. Liu, T.-S. Chua, Kgat: Knowledge graph attention network for recommendation, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 950\u2013958.","DOI":"10.1145\/3292500.3330989"},{"key":"10.1016\/j.jbi.2025.104861_b5","doi-asserted-by":"crossref","unstructured":"S. Zhou, X. Dai, H. Chen, W. Zhang, K. Ren, R. Tang, X. He, Y. Yu, Interactive recommender system via knowledge graph-enhanced reinforcement learning, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 179\u2013188.","DOI":"10.1145\/3397271.3401174"},{"key":"10.1016\/j.jbi.2025.104861_b6","doi-asserted-by":"crossref","unstructured":"B.Y. Lin, X. Chen, J. Chen, X. Ren, KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 2829\u20132839.","DOI":"10.18653\/v1\/D19-1282"},{"key":"10.1016\/j.jbi.2025.104861_b7","doi-asserted-by":"crossref","unstructured":"M. Yasunaga, H. Ren, A. Bosselut, P. Liang, J. Leskovec, QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering, in: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 535\u2013546.","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"10.1016\/j.jbi.2025.104861_b8","series-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021","first-page":"4038","article-title":"Learning contextualized knowledge structures for commonsense reasoning","author":"Yan","year":"2021"},{"key":"10.1016\/j.jbi.2025.104861_b9","series-title":"Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13\u201317, 2021, Proceedings, Part II 21","first-page":"466","article-title":"Zero-shot scene graph relation prediction through commonsense knowledge integration","author":"Kan","year":"2021"},{"issue":"5","key":"10.1016\/j.jbi.2025.104861_b10","doi-asserted-by":"crossref","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":"Nature Biotechnol."},{"key":"10.1016\/j.jbi.2025.104861_b11","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-01960-3","article-title":"Building a knowledge graph to enable precision medicine","author":"Chandak","year":"2023","journal-title":"Nat. Sci. Data"},{"issue":"11","key":"10.1016\/j.jbi.2025.104861_b12","doi-asserted-by":"crossref","DOI":"10.2196\/jmir.8073","article-title":"Patient health record systems scope and functionalities: literature review and future directions","volume":"19","author":"Bouayad","year":"2017","journal-title":"J. Med. Internet Res."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.jbi.2025.104861_b14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ijmedinf.2018.03.013","article-title":"Concurrence of big data analytics and healthcare: A systematic review","volume":"114","author":"Mehta","year":"2018","journal-title":"Int. J. Med. Informatics"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0217-0","article-title":"Big data in healthcare: management, analysis and future prospects","volume":"6","author":"Dash","year":"2019","journal-title":"J. Big Data"},{"key":"10.1016\/j.jbi.2025.104861_b16","doi-asserted-by":"crossref","unstructured":"J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"},{"key":"10.1016\/j.jbi.2025.104861_b17","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jbi.2025.104861_b18","doi-asserted-by":"crossref","unstructured":"J. Zhang, X. Song, Y. Zeng, J. Chen, J. Shen, Y. Mao, L. Li, Taxonomy completion via triplet matching network, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 4662\u20134670.","DOI":"10.1609\/aaai.v35i5.16596"},{"key":"10.1016\/j.jbi.2025.104861_b19","doi-asserted-by":"crossref","unstructured":"Y. Yu, Y. Li, J. Shen, H. Feng, J. Sun, C. Zhang, Steam: Self-supervised taxonomy expansion with mini-paths, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 1026\u20131035.","DOI":"10.1145\/3394486.3403145"},{"key":"10.1016\/j.jbi.2025.104861_b20","doi-asserted-by":"crossref","unstructured":"S. Wang, R. Zhao, X. Chen, Y. Zheng, B. Liu, Enquire one\u2019s parent and child before decision: Fully exploit hierarchical structure for self-supervised taxonomy expansion, in: Proceedings of the Web Conference 2021, 2021, pp. 3291\u20133304.","DOI":"10.1145\/3442381.3449948"},{"key":"10.1016\/j.jbi.2025.104861_b21","doi-asserted-by":"crossref","unstructured":"Q. Zeng, J. Lin, W. Yu, J. Cleland-Huang, M. Jiang, Enhancing taxonomy completion with concept generation via fusing relational representations, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 2104\u20132113.","DOI":"10.1145\/3447548.3467308"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b22","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41597-020-0543-2","article-title":"Building a PubMed knowledge graph","volume":"7","author":"Xu","year":"2020","journal-title":"Sci. Data"},{"key":"10.1016\/j.jbi.2025.104861_b23","series-title":"CEUR Workshop Proceedings","article-title":"Personalized health knowledge graph","volume":"Vol. 2317","author":"Gyrard","year":"2018"},{"key":"10.1016\/j.jbi.2025.104861_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101817","article-title":"Real-world data medical knowledge graph: construction and applications","volume":"103","author":"Li","year":"2020","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b25","doi-asserted-by":"crossref","DOI":"10.1515\/jib-2016-0002","article-title":"Knowledge discovery in biological databases for revealing candidate genes linked to complex phenotypes","volume":"14","author":"Hassani-Pak","year":"2017","journal-title":"J. Integr. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b26","series-title":"Personal health knowledge graph for clinically relevant diet recommendations","author":"Seneviratne","year":"2021"},{"issue":"5","key":"10.1016\/j.jbi.2025.104861_b27","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1002\/jso.23053","article-title":"Clinical decision support systems: potential with pitfalls","volume":"105","author":"Eberhardt","year":"2012","journal-title":"J. Surg. Oncol."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13336-015-0019-3","article-title":"Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine","volume":"5","author":"Castaneda","year":"2015","journal-title":"J. Clin. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b29","first-page":"1","article-title":"What is an ontology?","author":"Guarino","year":"2009","journal-title":"Handb. Ontol."},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.14778\/2078331.2078332","article-title":"PARIS: probabilistic alignment of relations, instances, and schema","volume":"5","author":"Suchanek","year":"2011","journal-title":"Proc. the VLDB Endow."},{"key":"10.1016\/j.jbi.2025.104861_b31","series-title":"E-Health\u2013for Continuity of Care","first-page":"283","article-title":"Formalizing mappings to optimize automated schema alignment: application to rare diseases","author":"Maaroufi","year":"2014"},{"key":"10.1016\/j.jbi.2025.104861_b32","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jbi.2025.104861_b33","doi-asserted-by":"crossref","unstructured":"H. Ye, N. Zhang, H. Chen, H. Chen, Generative Knowledge Graph Construction: A Review, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 1\u201317.","DOI":"10.18653\/v1\/2022.emnlp-main.1"},{"key":"10.1016\/j.jbi.2025.104861_b34","doi-asserted-by":"crossref","unstructured":"B. Shi, T. Weninger, Open-world knowledge graph completion, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018.","DOI":"10.1609\/aaai.v32i1.11535"},{"key":"10.1016\/j.jbi.2025.104861_b35","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","first-page":"4752","article-title":"Probabilistic case-based reasoning for open-world knowledge graph completion","author":"Das","year":"2020"},{"key":"10.1016\/j.jbi.2025.104861_b36","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s11280-020-00847-2","article-title":"Open-world knowledge graph completion with multiple interaction attention","volume":"24","author":"Niu","year":"2021","journal-title":"World Wide Web"},{"key":"10.1016\/j.jbi.2025.104861_b37","first-page":"1","article-title":"OERL: Enhanced representation learning via open knowledge graphs","author":"Li","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.jbi.2025.104861_b38","series-title":"4th Conference on Automated Knowledge Base Construction","article-title":"Open-world taxonomy and knowledge graph co-learning","author":"Lu","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b39","series-title":"Inductive Logic Programming","author":"Muggleton","year":"1992"},{"key":"10.1016\/j.jbi.2025.104861_b40","doi-asserted-by":"crossref","unstructured":"L.A. Gal\u00e1rraga, C. Teflioudi, K. Hose, F. Suchanek, AMIE: association rule mining under incomplete evidence in ontological knowledge bases, in: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 413\u2013422.","DOI":"10.1145\/2488388.2488425"},{"key":"10.1016\/j.jbi.2025.104861_b41","doi-asserted-by":"crossref","unstructured":"S. Kok, P. Domingos, Learning the structure of Markov logic networks, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 441\u2013448.","DOI":"10.1145\/1102351.1102407"},{"key":"10.1016\/j.jbi.2025.104861_b42","doi-asserted-by":"crossref","unstructured":"A. Saxena, A. Kochsiek, R. Gemulla, Sequence-to-Sequence Knowledge Graph Completion and Question Answering, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 2814\u20132828.","DOI":"10.18653\/v1\/2022.acl-long.201"},{"key":"10.1016\/j.jbi.2025.104861_b43","doi-asserted-by":"crossref","unstructured":"X. Xie, N. Zhang, Z. Li, S. Deng, H. Chen, F. Xiong, M. Chen, H. Chen, From discrimination to generation: knowledge graph completion with generative transformer, in: Companion Proceedings of the Web Conference 2022, 2022, pp. 162\u2013165.","DOI":"10.1145\/3487553.3524238"},{"key":"10.1016\/j.jbi.2025.104861_b44","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2022","first-page":"3833","article-title":"PALT: Parameter-lite transfer of language models for knowledge graph completion","author":"Shen","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b45","doi-asserted-by":"crossref","unstructured":"N. De Cao, L. Wu, K. Popat, M. Artetxe, N. Goyal, M. Plekhanov, L. Zettlemoyer, N. Cancedda, S. Riedel, F. Petroni, Multilingual autoregressive entity linking, Trans. Assoc. Comput. Linguist. 10, 274\u2013290.","DOI":"10.1162\/tacl_a_00460"},{"key":"10.1016\/j.jbi.2025.104861_b46","series-title":"Findings of the Association for Computational Linguistics: ACL 2022","first-page":"1972","article-title":"Detection, disambiguation, re-ranking: Autoregressive entity linking as a multi-task problem","author":"Mrini","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b47","doi-asserted-by":"crossref","unstructured":"Y.M. Cho, L. Zhang, C. Callison-Burch, Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 9394\u20139401.","DOI":"10.18653\/v1\/2022.emnlp-main.638"},{"key":"10.1016\/j.jbi.2025.104861_b48","doi-asserted-by":"crossref","unstructured":"Y. Lu, Q. Liu, D. Dai, X. Xiao, H. Lin, X. Han, L. Sun, H. Wu, Unified Structure Generation for Universal Information Extraction, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 5755\u20135772.","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"10.1016\/j.jbi.2025.104861_b49","doi-asserted-by":"crossref","unstructured":"Y. Zhuang, Y. Li, J. Zhang, Y. Yu, Y. Mou, X. Chen, L. Song, C. Zhang, ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 730\u2013744.","DOI":"10.18653\/v1\/2022.emnlp-main.46"},{"key":"10.1016\/j.jbi.2025.104861_b50","series-title":"International Conference on Machine Learning","first-page":"3929","article-title":"Retrieval augmented language model pre-training","author":"Guu","year":"2020"},{"key":"10.1016\/j.jbi.2025.104861_b51","series-title":"The Semantic Web\u2013ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26\u201330, 2019, Proceedings, Part II 18","first-page":"309","article-title":"Claimskg: A knowledge graph of fact-checked claims","author":"Tchechmedjiev","year":"2019"},{"key":"10.1016\/j.jbi.2025.104861_b52","series-title":"International Conference on Machine Learning","first-page":"27454","article-title":"Neural-symbolic models for logical queries on knowledge graphs","author":"Zhu","year":"2022"},{"issue":"6","key":"10.1016\/j.jbi.2025.104861_b53","doi-asserted-by":"crossref","first-page":"bbac404","DOI":"10.1093\/bib\/bbac404","article-title":"A review of biomedical datasets relating to drug discovery: a knowledge graph perspective","volume":"23","author":"Bonner","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104403","article-title":"Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study","volume":"143","author":"Murali","year":"2023","journal-title":"J. Biomed. Informatics"},{"issue":"8","key":"10.1016\/j.jbi.2025.104861_b55","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.3390\/cancers14081906","article-title":"Ontologies and knowledge graphs in oncology research","volume":"14","author":"Silva","year":"2022","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b56","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1186\/s40537-023-00774-9","article-title":"Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities","volume":"10","author":"Abu-Salih","year":"2023","journal-title":"J. Big Data"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b57","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1038\/s41597-022-01435-x","article-title":"A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks","volume":"9","author":"Blagec","year":"2022","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b58","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nature Genet."},{"issue":"D1","key":"10.1016\/j.jbi.2025.104861_b59","doi-asserted-by":"crossref","first-page":"D940","DOI":"10.1093\/nar\/gkr972","article-title":"Disease ontology: a backbone for disease semantic integration","volume":"40","author":"Schriml","year":"2012","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"10.1016\/j.jbi.2025.104861_b60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2005-6-2-r21","article-title":"An ontology for cell types","volume":"6","author":"Bard","year":"2005","journal-title":"Genome Biol."},{"issue":"6","key":"10.1016\/j.jbi.2025.104861_b61","doi-asserted-by":"crossref","first-page":"bbab282","DOI":"10.1093\/bib\/bbab282","article-title":"Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison","volume":"22","author":"Song","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03889-5","article-title":"BioRel: towards large-scale biomedical relation extraction","volume":"21","author":"Xing","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"01","key":"10.1016\/j.jbi.2025.104861_b63","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1055\/s-0040-1702001","article-title":"Medical information extraction in the age of deep learning","volume":"29","author":"Hahn","year":"2020","journal-title":"Yearb. Med. Informatics"},{"issue":"suppl_1","key":"10.1016\/j.jbi.2025.104861_b64","doi-asserted-by":"crossref","first-page":"D267","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":"10.1016\/j.jbi.2025.104861_b65","first-page":"279","article-title":"SNOMED-CT: The advanced terminology and coding system for ehealth","volume":"121","author":"Donnelly","year":"2006","journal-title":"Stud. Health Technol. Inform."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b66","doi-asserted-by":"crossref","first-page":"596","DOI":"10.3174\/ajnr.A4696","article-title":"ICD-10: history and context","volume":"37","author":"Hirsch","year":"2016","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.jbi.2025.104861_b67","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.26726","article-title":"Systematic integration of biomedical knowledge prioritizes drugs for repurposing","volume":"6","author":"Himmelstein","year":"2017","journal-title":"Elife"},{"key":"10.1016\/j.jbi.2025.104861_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2023.106460","article-title":"Biomedical discovery through the integrative biomedical knowledge hub (iBKH)","author":"Su","year":"2023","journal-title":"Iscience"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b69","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1038\/s41467-022-29993-z","article-title":"Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes","volume":"13","author":"Youn","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jbi.2025.104861_b70","series-title":"The Semantic Web\u2013ISWC 2014: 13th International Semantic Web Conference, Riva Del Garda, Italy, October 19-23, 2014. Proceedings, Part II 13","first-page":"17","article-title":"Towards annotating potential incoherences in bioportal mappings","author":"Faria","year":"2014"},{"key":"10.1016\/j.jbi.2025.104861_b71","series-title":"The Semantic Web\u2013ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23\u201327, 2022, Proceedings","first-page":"575","article-title":"Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching","author":"He","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2211-5","article-title":"FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining","volume":"19","author":"Bachman","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.jbi.2025.104861_b73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11704-020-8426-4","article-title":"An integrated pipeline model for biomedical entity alignment","volume":"15","author":"Hu","year":"2021","journal-title":"Front. Comput. Sci."},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b74","doi-asserted-by":"crossref","first-page":"155","DOI":"10.3390\/info14030155","article-title":"Tecre: A novel temporal conflict resolution method based on temporal knowledge graph embedding","volume":"14","author":"Ma","year":"2023","journal-title":"Inform."},{"key":"10.1016\/j.jbi.2025.104861_b75","doi-asserted-by":"crossref","unstructured":"C. Liang, Y. Yu, H. Jiang, S. Er, R. Wang, T. Zhao, C. Zhang, Bond: Bert-assisted open-domain named entity recognition with distant supervision, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 1054\u20131064.","DOI":"10.1145\/3394486.3403149"},{"key":"10.1016\/j.jbi.2025.104861_b76","unstructured":"Y. Huang, K. He, Y. Wang, X. Zhang, T. Gong, R. Mao, C. Li, Copner: Contrastive learning with prompt guiding for few-shot named entity recognition, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 2515\u20132527."},{"key":"10.1016\/j.jbi.2025.104861_b77","series-title":"QaNER: Prompting question answering models for few-shot named entity recognition","author":"Liu","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b78","unstructured":"J. Zhang, Z. Wang, S. Zhang, M.M. Bhalerao, Y. Liu, D. Zhu, S. Wang, GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates, in: The 37th AAAI Conference on Artificial Intelligence, 2023."},{"key":"10.1016\/j.jbi.2025.104861_b79","doi-asserted-by":"crossref","unstructured":"M. Agrawal, S. Hegselmann, H. Lang, Y. Kim, D. Sontag, Large language models are few-shot clinical information extractors, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 1998\u20132022.","DOI":"10.18653\/v1\/2022.emnlp-main.130"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b80","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1038\/s41467-021-22328-4","article-title":"Ontology-driven weak supervision for clinical entity classification in electronic health records","volume":"12","author":"Fries","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jbi.2025.104861_b81","series-title":"Zero-shot clinical entity recognition using ChatGPT","author":"Hu","year":"2023"},{"key":"10.1016\/j.jbi.2025.104861_b82","doi-asserted-by":"crossref","unstructured":"T. Zhu, Y. Qin, Q. Chen, B. Hu, Y. Xiang, Enhancing Entity Representations with Prompt Learning for Biomedical Entity Linking, in: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022, pp. 4036\u20134042.","DOI":"10.24963\/ijcai.2022\/560"},{"key":"10.1016\/j.jbi.2025.104861_b83","doi-asserted-by":"crossref","unstructured":"J. Lu, J. Shen, B. Xiong, W. Ma, S. Steffen, C. Yang, [HiPrompt]: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval - Short Paper, in: SIGIR 2023, 2023.","DOI":"10.1145\/3539618.3591997"},{"issue":"18","key":"10.1016\/j.jbi.2025.104861_b84","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1093\/bioinformatics\/btab207","article-title":"SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization","volume":"37","author":"Yu","year":"2021","journal-title":"Bioinform."},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b85","doi-asserted-by":"crossref","first-page":"bbac140","DOI":"10.1093\/bib\/bbac140","article-title":"Attention-based knowledge graph representation learning for predicting drug-drug interactions","volume":"23","author":"Su","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"7","key":"10.1016\/j.jbi.2025.104861_b86","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004259","article-title":"Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes","volume":"11","author":"Himmelstein","year":"2015","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.jbi.2025.104861_b87","series-title":"DRKG - drug repurposing knowledge graph for Covid-19","author":"Ioannidis","year":"2020"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b88","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: kyoto encyclopedia of genes and genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"10.1016\/j.jbi.2025.104861_b89","doi-asserted-by":"crossref","first-page":"D638","DOI":"10.1093\/nar\/gkac1000","article-title":"The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest","volume":"51","author":"Szklarczyk","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jbi.2025.104861_b90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13326-016-0088-7","article-title":"The cell ontology 2016: enhanced content, modularization, and ontology interoperability","volume":"7","author":"Diehl","year":"2016","journal-title":"J. Biomed. Semant."},{"key":"10.1016\/j.jbi.2025.104861_b91","first-page":"1","article-title":"Generating novel molecule for target protein (SARS-CoV-2) using drug\u2013target interaction based on graph neural network","volume":"11","author":"Ranjan","year":"2022","journal-title":"Netw. Model. Anal. Heal. Informatics Bioinform."},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b92","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.3390\/molecules27031039","article-title":"Prediction of compound synthesis accessibility based on reaction knowledge graph","volume":"27","author":"Li","year":"2022","journal-title":"Mol."},{"key":"10.1016\/j.jbi.2025.104861_b93","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtice.2021.07.015","article-title":"Intelligent generation of optimal synthetic pathways based on knowledge graph inference and retrosynthetic predictions using reaction big data","volume":"130","author":"Jeong","year":"2022","journal-title":"J. the Taiwan Inst. Chem. Eng."},{"key":"10.1016\/j.jbi.2025.104861_b94","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2021.103696","article-title":"Drug repurposing for COVID-19 via knowledge graph completion","volume":"115","author":"Zhang","year":"2021","journal-title":"J. Biomed. Informatics"},{"key":"10.1016\/j.jbi.2025.104861_b95","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2022.104133","article-title":"Kg-predict: a knowledge graph computational framework for drug repurposing","volume":"132","author":"Gao","year":"2022","journal-title":"J. Biomed. Informatics"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b96","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/fi13010014","article-title":"Drug repurposing for parkinson\u2019s disease by integrating knowledge graph completion model and knowledge fusion of medical literature","volume":"13","author":"Zhang","year":"2021","journal-title":"Futur. Internet"},{"key":"10.1016\/j.jbi.2025.104861_b97","doi-asserted-by":"crossref","DOI":"10.1021\/acs.jcim.2c01291","article-title":"DrugRep-KG: Toward learning a unified latent space for drug repurposing using knowledge graphs","author":"Ghorbanali","year":"2023","journal-title":"J. Chem. Inf. Model."},{"issue":"D1","key":"10.1016\/j.jbi.2025.104861_b98","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.jbi.2025.104861_b99","series-title":"2014 IEEE 30th International Conference on Data Engineering","first-page":"1254","article-title":"Knowlife: a knowledge graph for health and life sciences","author":"Ernst","year":"2014"},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b100","doi-asserted-by":"crossref","first-page":"bbaa344","DOI":"10.1093\/bib\/bbaa344","article-title":"PharmKG: a dedicated knowledge graph benchmark for bomedical data mining","volume":"22","author":"Zheng","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"12","key":"10.1016\/j.jbi.2025.104861_b101","doi-asserted-by":"crossref","first-page":"4968","DOI":"10.1021\/acs.jcim.9b00683","article-title":"ROBOKOP KG and KGB: integrated knowledge graphs from federated sources","volume":"59","author":"Bizon","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.jbi.2025.104861_b102","doi-asserted-by":"crossref","unstructured":"P. Wang, T. Shi, C.K. Reddy, Text-to-SQL generation for question answering on electronic medical records, in: Proceedings of the Web Conference 2020, 2020, pp. 350\u2013361.","DOI":"10.1145\/3366423.3380120"},{"key":"10.1016\/j.jbi.2025.104861_b103","series-title":"One model for all domains: Collaborative domain-prefix tuning for cross-domain NER","author":"Chen","year":"2023"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b104","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/s41746-022-00742-2","article-title":"A large language model for electronic health records","volume":"5","author":"Yang","year":"2022","journal-title":"Npj Digit. Med."},{"key":"10.1016\/j.jbi.2025.104861_b105","series-title":"Proceedings of the Conference on Health, Inference, and Learning","first-page":"138","article-title":"Uncertainty-aware text-to-program for question answering on structured electronic health records","volume":"vol. 174","author":"Kim","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b106","doi-asserted-by":"crossref","unstructured":"R. Xu, Y. Yu, J.C. Ho, C. Yang, Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training, in: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023.","DOI":"10.1145\/3539618.3592085"},{"key":"10.1016\/j.jbi.2025.104861_b107","series-title":"REPLUG: Retrieval-augmented black-box language models","author":"Shi","year":"2023"},{"key":"10.1016\/j.jbi.2025.104861_b108","doi-asserted-by":"crossref","unstructured":"N. Vedula, S. Parthasarathy, Face-keg: Fact checking explained using knowledge graphs, in: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021, pp. 526\u2013534.","DOI":"10.1145\/3437963.3441828"},{"key":"10.1016\/j.jbi.2025.104861_b109","series-title":"2022 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining","first-page":"47","article-title":"DEAP-FAKED: Knowledge graph based approach for fake news detection","author":"Mayank","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b110","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2020.621766","article-title":"Identifying ingredient substitutions using a knowledge graph of food","volume":"3","author":"Shirai","year":"2021","journal-title":"Front. Artif. Intell."},{"key":"10.1016\/j.jbi.2025.104861_b111","series-title":"Computational Science and Its Applications\u2013ICCSA 2022 Workshops: Malaga, Spain, July 4\u20137, 2022, Proceedings, Part I","first-page":"138","article-title":"Empowering COVID-19 fact-checking with extended knowledge graphs","author":"Mengoni","year":"2022"},{"issue":"17","key":"10.1016\/j.jbi.2025.104861_b112","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1093\/bioinformatics\/btx275","article-title":"Neuro-symbolic representation learning on biological knowledge graphs","volume":"33","author":"Alshahrani","year":"2017","journal-title":"Bioinform."},{"issue":"7139","key":"10.1016\/j.jbi.2025.104861_b113","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1038\/446975a","article-title":"When good drugs go bad","volume":"446","author":"Giacomini","year":"2007","journal-title":"Nature"},{"key":"10.1016\/j.jbi.2025.104861_b114","doi-asserted-by":"crossref","unstructured":"M.R. Karim, M. Cochez, J.B. Jares, M. Uddin, O. Beyan, S. Decker, Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network, in: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2019, pp. 113\u2013123.","DOI":"10.1145\/3307339.3342161"},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b115","doi-asserted-by":"crossref","first-page":"bbaa256","DOI":"10.1093\/bib\/bbaa256","article-title":"Drug\u2013drug interaction prediction with wasserstein adversarial autoencoder-based knowledge graph embeddings","volume":"22","author":"Dai","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b116","series-title":"IJCAI","first-page":"2739","article-title":"KGNN: Knowledge graph neural network for drug-drug interaction prediction.","volume":"Vol. 380","author":"Lin","year":"2020"},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b117","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug\u2013target interaction prediction: databases, web servers and computational models","volume":"17","author":"Chen","year":"2016","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b118","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110151","article-title":"GA-ens: A novel drug\u2013target interactions prediction method by incorporating prior knowledge graph into dual wasserstein generative adversarial network with gradient penalty","volume":"139","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jbi.2025.104861_b119","series-title":"2021 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"588","article-title":"Discovering DTI and DDI by knowledge graph with MHRW and improved neural network","author":"Zhang","year":"2021"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b120","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1007\/s10489-021-02454-8","article-title":"KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and alzheimer\u2019s disease drug repositions","volume":"52","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b121","doi-asserted-by":"crossref","first-page":"6775","DOI":"10.1038\/s41467-021-27137-3","article-title":"A unified drug\u2013target interaction prediction framework based on knowledge graph and recommendation system","volume":"12","author":"Ye","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jbi.2025.104861_b122","series-title":"DDN2.0: R and python packages for differential dependency network analysis of biological systems","author":"Zhang","year":"2021"},{"key":"10.1016\/j.jbi.2025.104861_b123","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baad006","article-title":"AIMedGraph: A comprehensive multi-relational knowledge graph for precision medicine","volume":"2023","author":"Quan","year":"2023","journal-title":"Database"},{"key":"10.1016\/j.jbi.2025.104861_b124","series-title":"Single-Cell Genomics: Coming of Age","first-page":"1","author":"Linnarsson","year":"2016"},{"issue":"D1","key":"10.1016\/j.jbi.2025.104861_b125","doi-asserted-by":"crossref","first-page":"D97","DOI":"10.1093\/nar\/gkaa995","article-title":"Grndb: decoding the gene regulatory networks in diverse human and mouse conditions","volume":"49","author":"Fang","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"10.1016\/j.jbi.2025.104861_b126","doi-asserted-by":"crossref","first-page":"D950","DOI":"10.1093\/nar\/gkac957","article-title":"Genomickb: a knowledge graph for the human genome","volume":"51","author":"Feng","year":"2023","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"10.1016\/j.jbi.2025.104861_b127","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1377\/hlthaff.25.2.420","article-title":"Estimating the cost of new drug development: is it really $802 million?","volume":"25","author":"Adams","year":"2006","journal-title":"Health Aff."},{"issue":"20","key":"10.1016\/j.jbi.2025.104861_b128","doi-asserted-by":"crossref","DOI":"10.1056\/NEJMc1504317","article-title":"The cost of drug development","volume":"372","author":"DiMasi","year":"2015","journal-title":"N. Engl. J. Med."},{"issue":"9","key":"10.1016\/j.jbi.2025.104861_b129","doi-asserted-by":"crossref","first-page":"549","DOI":"10.3906\/sag-2004-127","article-title":"Coronaviruses and sars-cov-2","volume":"50","author":"Has\u00f6ks\u00fcz","year":"2020","journal-title":"Turk. J. Med. Sci."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b130","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.1177\/1460458220937101","article-title":"Knowledge-driven drug repurposing using a comprehensive drug knowledge graph","volume":"26","author":"Zhu","year":"2020","journal-title":"Heal. Informatics J."},{"issue":"9","key":"10.1016\/j.jbi.2025.104861_b131","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1080\/17460441.2021.1910673","article-title":"Knowledge graphs and their applications in drug discovery","volume":"16","author":"MacLean","year":"2021","journal-title":"Expert. Opin. Drug Discov."},{"key":"10.1016\/j.jbi.2025.104861_b132","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12920-019-0627-z","article-title":"A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network","volume":"12","author":"Xu","year":"2019","journal-title":"BMC Med. Genom."},{"issue":"6","key":"10.1016\/j.jbi.2025.104861_b133","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nrd.2017.70","article-title":"How much do clinical trials cost","volume":"16","author":"Martin","year":"2017","journal-title":"Nat Rev Drug Discov"},{"issue":"10","key":"10.1016\/j.jbi.2025.104861_b134","doi-asserted-by":"crossref","first-page":"e549","DOI":"10.1016\/S2589-7500(20)30219-3","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"2","author":"Rivera","year":"2020","journal-title":"Lancet Digit. Heal."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b135","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1111\/cts.12764","article-title":"Clinical trial generalizability assessment in the big data era: a review","volume":"13","author":"He","year":"2020","journal-title":"Clin. Transl. Sci."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b136","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1093\/jamia\/ocy178","article-title":"Criteria2Query: a natural language interface to clinical databases for cohort definition","volume":"26","author":"Yuan","year":"2019","journal-title":"J. Am. Med. Informatics Assoc."},{"key":"10.1016\/j.jbi.2025.104861_b137","series-title":"Information extraction of clinical trial eligibility criteria","author":"Tseo","year":"2020"},{"issue":"7855","key":"10.1016\/j.jbi.2025.104861_b138","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1038\/s41586-021-03430-5","article-title":"Evaluating eligibility criteria of oncology trials using real-world data and AI","volume":"592","author":"Liu","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.jbi.2025.104861_b139","doi-asserted-by":"crossref","unstructured":"J. Gao, C. Xiao, L.M. Glass, J. Sun, COMPOSE: cross-modal pseudo-siamese network for patient trial matching, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 803\u2013812.","DOI":"10.1145\/3394486.3403123"},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b140","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2022.100445","article-title":"HINT: Hierarchical interaction network for clinical-trial-outcome predictions","volume":"3","author":"Fu","year":"2022","journal-title":"Patterns"},{"key":"10.1016\/j.jbi.2025.104861_b141","series-title":"SPOT: Sequential predictive modeling of clinical trial outcome with meta-learning","author":"Wang","year":"2023"},{"key":"10.1016\/j.jbi.2025.104861_b142","doi-asserted-by":"crossref","unstructured":"J. Mullenbach, S. Wiegreffe, J. Duke, J. Sun, J. Eisenstein, Explainable Prediction of Medical Codes from Clinical Text, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, pp. 1101\u20131111.","DOI":"10.18653\/v1\/N18-1100"},{"key":"10.1016\/j.jbi.2025.104861_b143","doi-asserted-by":"crossref","unstructured":"Z. Zhang, J. Liu, N. Razavian, BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining, in: Proceedings of the 3rd Clinical Natural Language Processing Workshop, 2020, pp. 24\u201334.","DOI":"10.18653\/v1\/2020.clinicalnlp-1.3"},{"key":"10.1016\/j.jbi.2025.104861_b144","doi-asserted-by":"crossref","unstructured":"T. Vu, D.Q. Nguyen, A. Nguyen, A label attention model for ICD coding from clinical text, in: Proceedings of the Twenty-Ninth International Joint Conferences on Artificial Intelligence, 2021, pp. 3335\u20133341.","DOI":"10.24963\/ijcai.2020\/461"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b145","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1038\/s41746-022-00705-7","article-title":"Automated clinical coding: what, why, and where we are?","volume":"5","author":"Dong","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.jbi.2025.104861_b146","series-title":"Proceedings of the 6th Machine Learning for Healthcare Conference","first-page":"196","article-title":"Read, attend, and code: Pushing the limits of medical codes prediction from clinical notes by machines","volume":"vol. 149","author":"Kim","year":"2021"},{"key":"10.1016\/j.jbi.2025.104861_b147","doi-asserted-by":"crossref","unstructured":"X. Xie, Y. Xiong, P.S. Yu, Y. Zhu, Ehr coding with multi-scale feature attention and structured knowledge graph propagation, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 649\u2013658.","DOI":"10.1145\/3357384.3357897"},{"key":"10.1016\/j.jbi.2025.104861_b148","doi-asserted-by":"crossref","unstructured":"P. Cao, Y. Chen, K. Liu, J. Zhao, S. Liu, W. Chong, Hypercore: Hyperbolic and co-graph representation for automatic icd coding, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 3105\u20133114.","DOI":"10.18653\/v1\/2020.acl-main.282"},{"key":"10.1016\/j.jbi.2025.104861_b149","unstructured":"B. Min, R. Grishman, L. Wan, C. Wang, D. Gondek, Distant supervision for relation extraction with an incomplete knowledge base, in: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013, pp. 777\u2013782."},{"key":"10.1016\/j.jbi.2025.104861_b150","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"Int. Conf. Learn. Represent. ( ICLR)"},{"key":"10.1016\/j.jbi.2025.104861_b151","doi-asserted-by":"crossref","unstructured":"J. Lu, L. Du, M. Liu, J. Dipnall, Multi-label Few\/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2020, pp. 2935\u20132943.","DOI":"10.18653\/v1\/2020.emnlp-main.235"},{"key":"10.1016\/j.jbi.2025.104861_b152","series-title":"Proceedings of the 7th Machine Learning for Healthcare Conference","first-page":"198","article-title":"Hicu: Leveraging hierarchy for curriculum learning in automated icd coding","volume":"vol. 182","author":"Ren","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b153","series-title":"Exploring partial knowledge base inference in biomedical entity linking","author":"Yuan","year":"2023"},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b154","doi-asserted-by":"crossref","first-page":"bbaa110","DOI":"10.1093\/bib\/bbaa110","article-title":"Enriching contextualized language model from knowledge graph for biomedical information extraction","volume":"22","author":"Fei","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b155","doi-asserted-by":"crossref","unstructured":"A. Roy, S. Pan, Incorporating medical knowledge in BERT for clinical relation extraction, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, pp. 5357\u20135366.","DOI":"10.18653\/v1\/2021.emnlp-main.435"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b156","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1038\/s41746-021-00519-z","article-title":"Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data","volume":"4","author":"Hong","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.jbi.2025.104861_b157","series-title":"Multimodal learning on graphs for disease relation extraction","author":"Lin","year":"2022"},{"key":"10.1016\/j.jbi.2025.104861_b158","doi-asserted-by":"crossref","unstructured":"E. Choi, M.T. Bahadori, L. Song, W.F. Stewart, J. Sun, GRAM: graph-based attention model for healthcare representation learning, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 787\u2013795.","DOI":"10.1145\/3097983.3098126"},{"key":"10.1016\/j.jbi.2025.104861_b159","doi-asserted-by":"crossref","unstructured":"F. Ma, Q. You, H. Xiao, R. Chitta, J. Zhou, J. Gao, Kame: Knowledge-based attention model for diagnosis prediction in healthcare, in: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, pp. 743\u2013752.","DOI":"10.1145\/3269206.3271701"},{"issue":"10","key":"10.1016\/j.jbi.2025.104861_b160","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.3201\/eid1210.060016","article-title":"ICD-9 codes and surveillance for clostridium difficile\u2013associated disease","volume":"12","author":"Dubberke","year":"2006","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.jbi.2025.104861_b161","series-title":"2019 IEEE International Conference on Data Mining","first-page":"738","article-title":"Domain knowledge guided deep learning with electronic health records","author":"Yin","year":"2019"},{"key":"10.1016\/j.jbi.2025.104861_b162","series-title":"2019 IEEE International Conference on Data Mining","first-page":"1492","article-title":"KnowRisk: an interpretable knowledge-guided model for disease risk prediction","author":"Zhang","year":"2019"},{"key":"10.1016\/j.jbi.2025.104861_b163","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-015-0549-5","article-title":"Knowlife: a versatile approach for constructing a large knowledge graph for biomedical sciences","volume":"16","author":"Ernst","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.jbi.2025.104861_b164","doi-asserted-by":"crossref","unstructured":"M. Ye, S. Cui, Y. Wang, J. Luo, C. Xiao, F. Ma, Medpath: Augmenting health risk prediction via medical knowledge paths, in: Proceedings of the Web Conference 2021, 2021, pp. 1397\u20131409.","DOI":"10.1145\/3442381.3449860"},{"key":"10.1016\/j.jbi.2025.104861_b165","series-title":"2021 IEEE International Conference on Data Mining","first-page":"777","article-title":"Predictive modeling of clinical events with mutual enhancement between longitudinal patient records and medical knowledge graph","author":"Xu","year":"2021"},{"key":"10.1016\/j.jbi.2025.104861_b166","doi-asserted-by":"crossref","unstructured":"Y. Zhang, R. Chen, J. Tang, W.F. Stewart, J. Sun, LEAP: learning to prescribe effective and safe treatment combinations for multimorbidity, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 1315\u20131324.","DOI":"10.1145\/3097983.3098109"},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b167","first-page":"1","article-title":"Personalizing medication recommendation with a graph-based approach","volume":"40","author":"Bhoi","year":"2021","journal-title":"ACM Trans. Inf. Syst. ( TOIS)"},{"key":"10.1016\/j.jbi.2025.104861_b168","doi-asserted-by":"crossref","unstructured":"J. Shang, C. Xiao, T. Ma, H. Li, J. Sun, Gamenet: Graph augmented memory networks for recommending medication combination, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 1126\u20131133.","DOI":"10.1609\/aaai.v33i01.33011126"},{"key":"10.1016\/j.jbi.2025.104861_b169","doi-asserted-by":"crossref","unstructured":"J. Shang, T. Ma, C. Xiao, J. Sun, Pre-training of graph augmented transformers for medication recommendation, in: Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 5953\u20135959.","DOI":"10.24963\/ijcai.2019\/825"},{"key":"10.1016\/j.jbi.2025.104861_b170","doi-asserted-by":"crossref","unstructured":"J. Wu, B. Qian, Y. Li, Z. Gao, M. Ju, Y. Yang, Y. Zheng, T. Gong, C. Li, X. Zhang, Leveraging multiple types of domain knowledge for safe and effective drug recommendation, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 2169\u20132178.","DOI":"10.1145\/3511808.3557380"},{"key":"10.1016\/j.jbi.2025.104861_b171","doi-asserted-by":"crossref","unstructured":"Y. Tan, C. Kong, L. Yu, P. Li, C. Chen, X. Zheng, V.S. Hertzberg, C. Yang, 4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 3970\u20133980.","DOI":"10.1145\/3534678.3539089"},{"key":"10.1016\/j.jbi.2025.104861_b172","series-title":"Knowledge-driven new drug recommendation","author":"Wu","year":"2022"},{"issue":"11","key":"10.1016\/j.jbi.2025.104861_b173","doi-asserted-by":"crossref","first-page":"28373","DOI":"10.1007\/s11356-023-25237-9","article-title":"Knowledge graph of wastewater-based epidemiology development: A data-driven analysis based on research topics and trends","volume":"30","author":"Gao","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"9","key":"10.1016\/j.jbi.2025.104861_b174","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1093\/bioinformatics\/btaa834","article-title":"COVID-19 knowledge graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology","volume":"37","author":"Domingo-Fern\u00e1ndez","year":"2021","journal-title":"Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b175","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1085","article-title":"Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in wikidata","volume":"8","author":"Turki","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.jbi.2025.104861_b176","series-title":"Artificial Intelligence in Medicine: 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14\u201317, 2022, Proceedings","first-page":"47","article-title":"Ontological representation of causal relations for a deep understanding of associations between variables in epidemiology","author":"Pressat Laffouilh\u00e8re","year":"2022"},{"issue":"7","key":"10.1016\/j.jbi.2025.104861_b177","doi-asserted-by":"crossref","DOI":"10.2196\/26714","article-title":"A biomedical knowledge graph system to propose mechanistic hypotheses for real-world environmental health observations: cohort study and informatics application","volume":"9","author":"Fecho","year":"2021","journal-title":"JMIR Med. Inform."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b178","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/toxsci\/kfaa025","article-title":"A survey of systematic evidence mapping practice and the case for knowledge graphs in environmental health and toxicology","volume":"175","author":"Wolffe","year":"2020","journal-title":"Toxicol. Sci."},{"key":"10.1016\/j.jbi.2025.104861_b179","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.2147\/RMHP.S309732","article-title":"Construct a knowledge graph for China coronavirus (COVID-19) patient information tracking","author":"Wu","year":"2021","journal-title":"Risk Manag. Heal. Policy"},{"key":"10.1016\/j.jbi.2025.104861_b180","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117026","article-title":"Improving chronic disease management for children with knowledge graphs and artificial intelligence","volume":"201","author":"Yu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.jbi.2025.104861_b181","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TMM.2020.3046867","article-title":"Building and using personal knowledge graph to improve suicidal ideation detection on social media","volume":"24","author":"Cao","year":"2020","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b182","first-page":"495","article-title":"Knowledge graph analysis and visualization of research trends on driver behavior","volume":"38","author":"Liu","year":"2020","journal-title":"J. Intell. Fuzzy Systems"},{"key":"10.1016\/j.jbi.2025.104861_b183","doi-asserted-by":"crossref","unstructured":"S. Wang, Y. Zhang, B. Lin, B. Li, Interpretable emotion analysis based on knowledge graph and OCC model, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 2038\u20132045.","DOI":"10.1145\/3511808.3557365"},{"issue":"9","key":"10.1016\/j.jbi.2025.104861_b184","doi-asserted-by":"crossref","first-page":"btae560","DOI":"10.1093\/bioinformatics\/btae560","article-title":"Biomedical knowledge graph-optimized prompt generation for large language models","volume":"40","author":"Soman","year":"2024","journal-title":"Bioinform."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b185","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s13326-023-00301-y","article-title":"Bioblp: a modular framework for learning on multimodal biomedical knowledge graphs","volume":"14","author":"Daza","year":"2023","journal-title":"J. Biomed. Semant."},{"issue":"4","key":"10.1016\/j.jbi.2025.104861_b186","doi-asserted-by":"crossref","first-page":"btae163","DOI":"10.1093\/bioinformatics\/btae163","article-title":"Advancing entity recognition in biomedicine via instruction tuning of large language models","volume":"40","author":"Keloth","year":"2024","journal-title":"Bioinform."},{"key":"10.1016\/j.jbi.2025.104861_b187","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2024.3453956","article-title":"Geometric molecular graph representation learning model for drug-drug interactions prediction","author":"Jiang","year":"2024","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b188","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MIC.2020.3031769","article-title":"Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?","volume":"25","author":"Gaur","year":"2021","journal-title":"IEEE Internet Comput."},{"issue":"1","key":"10.1016\/j.jbi.2025.104861_b189","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/MIC.2021.3133551","article-title":"Causalkg: Causal knowledge graph explainability using interventional and counterfactual reasoning","volume":"26","author":"Jaimini","year":"2022","journal-title":"IEEE Internet Comput."},{"issue":"3","key":"10.1016\/j.jbi.2025.104861_b190","first-page":"146","article-title":"Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study","volume":"2","author":"Yang","year":"2021","journal-title":"J. Saf. Sci. Resil."},{"key":"10.1016\/j.jbi.2025.104861_b191","series-title":"Large language models encode clinical knowledge","author":"Singhal","year":"2022"},{"issue":"7","key":"10.1016\/j.jbi.2025.104861_b192","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1136\/bjophthalmol-2022-321141","article-title":"New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology","volume":"106","author":"Nath","year":"2022","journal-title":"Br. J. Ophthalmol."},{"issue":"7956","key":"10.1016\/j.jbi.2025.104861_b193","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046425000905?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046425000905?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T13:30:10Z","timestamp":1772890210000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046425000905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":193,"alternative-id":["S1532046425000905"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2025.104861","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A review on knowledge graphs for healthcare: Resources, applications, and promises","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2025.104861","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104861"}}