{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:25:47Z","timestamp":1768569947142,"version":"3.49.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Biomedical knowledge graphs (KGs) are crucial for drug discovery and disease understanding, yet their completion and reasoning are challenging. Knowledge embedding (KE) methods capture global semantics but struggle with dynamic structural integration, while graph neural networks (GNNs) excel locally but often lack semantic understanding. Even ensemble approaches, including those leveraging language models, often fail to achieve a deep, adaptive, and synergistic co-evolution between semantic comprehension and structural learning. Addressing this critical gap in fostering continuous, reciprocal refinement between these two aspects in complex biomedical KGs is paramount.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce BioGraphFusion, a novel framework for deeply synergistic semantic and structural learning. BioGraphFusion establishes a global semantic foundation via tensor decomposition, guiding an LSTM-driven mechanism to dynamically refine relation embeddings during graph propagation. This fosters adaptive interplay between semantic understanding and structural learning, further enhanced by query-guided subgraph construction and a hybrid scoring mechanism. Experiments across three key biomedical tasks demonstrate BioGraphFusion\u2019s superior performance over state-of-the-art KE, GNN, and ensemble models. A case study on cutaneous malignant melanoma 1 highlights its ability to unveil biologically meaningful pathways.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code and all data underlying this article are freely available in the GitHub repository at https:\/\/github.com\/Y-TARL\/BioGraphFusion.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf408","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T18:21:01Z","timestamp":1752862861000},"source":"Crossref","is-referenced-by-count":1,"title":["BioGraphFusion: graph knowledge embedding for biological completion and reasoning"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0481-2340","authenticated-orcid":false,"given":"Yitong","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology , 288 Liuhe Road, Xihu District , Hangzhou, Zhejiang Province, 310023,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4871-6345","authenticated-orcid":false,"given":"Jiaying","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology , 288 Liuhe Road, Xihu District , Hangzhou, Zhejiang Province, 310023,","place":["China"]}]},{"given":"Jiahe","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology , 288 Liuhe Road, Xihu District , Hangzhou, Zhejiang Province, 310023,","place":["China"]}]},{"given":"Xinnan","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology , 288 Liuhe Road, Xihu District , Hangzhou, Zhejiang Province, 310023,","place":["China"]}]},{"given":"Jianwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology , 288 Liuhe Road, Xihu District , Hangzhou, Zhejiang Province, 310023,","place":["China"]}]},{"given":"Tao","family":"Bo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrine Glucose & Lipids Metabolism, Department of Endocrinology, , Shandong Provincial Hospital Affiliated to Shandong First Medical University , 324 Jingwu Road, Huaiyin District , Jinan, Shandong Province, 250021,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"2025073102363430000_btaf408-B1","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1093\/hmg\/10.16.1701","article-title":"The melanocortin-1-receptor gene is the major freckle gene","volume":"10","author":"Bastiaens","year":"2001","journal-title":"Hum Mol 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