{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T20:57:07Z","timestamp":1781729827454,"version":"3.54.5"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFF1106400"],"award-info":[{"award-number":["2024YFF1106400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2541028"],"award-info":[{"award-number":["T2541028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373166"],"award-info":[{"award-number":["62373166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National First-class Discipline Program of Light Industry Technology and Engineering","award":["QGJC20230102"],"award-info":[{"award-number":["QGJC20230102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Genome-scale metabolic models (GEMs) can effectively facilitate many fields in synthetic biology, biomanufacturing, and biomedicine. Reconstructing high-quality GEMs is crucial for accurate phenotype predictions of organisms. However, draft GEMs generated by automated reconstruction tools contain many knowledge gaps, especially missing reactions. The existing machine learning-based gap-filling approaches need to be further developed. In this article, we propose a novel HyperGraph Learning approach with Multi-dimensional metabolite feature extractions and static\u2013dynamic Attention mechanisms (HGLMA) for predicting and teasing out missing reactions in GEM gap-fillings. HGLMA simultaneously uses two pretrained language models to proceed multi-dimensional metabolite feature extractions, which are further fused and regarded as node embeddings for graph learning. The directed and high-order associations between metabolites in reactions of GEMs are deeply mined by successively employing a directional graph network and a hypergraph neural network. Before outputting the predicted confidence score for candidate reactions, the static\u2013dynamic multi-head attention mechanism is utilized to automatically learn attention weights and to identify key metabolites within any candidate reaction. The five-fold cross-validation results on 108 BiGG GEMs show that HGLMA significantly outperforms other state-of-the-art machine learning-based approaches both in prediction performances and in the ability of discovering missing reactions from metabolic reaction pools. The ablation study shows the contributions of multi-dimensional feature extractions and static\u2013dynamic attention mechanisms. In addition, the phenotype prediction results of 24 bacterial organisms demonstrate the effectiveness and superiority of gap-fillings by HGLMA.<\/jats:p>","DOI":"10.1093\/bib\/bbag314","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T11:43:45Z","timestamp":1779968625000},"source":"Crossref","is-referenced-by-count":0,"title":["Hypergraph learning with multi-dimensional metabolite feature extractions and static\u2013dynamic attention mechanisms to fill missing reactions in metabolic networks"],"prefix":"10.1093","volume":"27","author":[{"given":"Kai","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Automation and Intelligent Science (School of Internet of Things), Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Qu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Automation and Intelligent Science (School of Internet of Things), Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Automation and Intelligent Science (School of Internet of Things), Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Luan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Automation and Intelligent Science (School of Internet of Things), Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Science Center for Future Foods, Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]},{"name":"Key Laboratory of Industrial Biotechnology, Ministry of Education and School of Biotechnology, Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]},{"name":"Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 ,","place":["China"]},{"name":"Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University , 1800 Lihu Road, Wuxi, Jiangsu 214122 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