{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:20:21Z","timestamp":1777868421941,"version":"3.51.4"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010616","name":"Qiqihar University","doi-asserted-by":"publisher","award":["145209206"],"award-info":[{"award-number":["145209206"]}],"id":[{"id":"10.13039\/501100010616","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["BioSystems"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.biosystems.2026.105729","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T15:52:48Z","timestamp":1771084368000},"page":"105729","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["KMGTMDA: KAN-driven multiscale graph neural network and context-enhanced prediction for human microbe\u2013disease associations"],"prefix":"10.1016","volume":"262","author":[{"given":"Xiaoxin","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YiPing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"WenLong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LiSen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.biosystems.2026.105729_b1","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M., 2019. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2623\u20132631.","DOI":"10.1145\/3292500.3330701"},{"issue":"6","key":"10.1016\/j.biosystems.2026.105729_b2","doi-asserted-by":"crossref","first-page":"bbae550","DOI":"10.1093\/bib\/bbae550","article-title":"Predicting disease-associated microbes based on similarity fusion and deep learning","volume":"25","author":"Chen","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.biosystems.2026.105729_b3","doi-asserted-by":"crossref","first-page":"bbaf075","DOI":"10.1093\/bib\/bbaf075","article-title":"Ensemble learning based on matrix completion improves microbe-disease association prediction","volume":"26","author":"Chen","year":"2025","journal-title":"Brief. Bioinform."},{"issue":"5","key":"10.1016\/j.biosystems.2026.105729_b4","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1093\/bioinformatics\/btw715","article-title":"A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases","volume":"33","author":"Chen","year":"2017","journal-title":"Bioinformatics"},{"key":"10.1016\/j.biosystems.2026.105729_b5","article-title":"Metapath aggregated graph neural network and tripartite heterogeneous networks for microbe-disease prediction","volume":"13","author":"Chen","year":"2022","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.biosystems.2026.105729_b6","article-title":"The female vaginal microbiome in health and bacterial vaginosis","volume":"11","author":"Chen","year":"2021","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"10.1016\/j.biosystems.2026.105729_b7","series-title":"A Power Primer","author":"Cohen","year":"2016"},{"key":"10.1016\/j.biosystems.2026.105729_b8","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.ejogrb.2019.12.035","article-title":"Bacterial vaginosis\u2014A brief synopsis of the literature","volume":"245","author":"Coudray","year":"2020","journal-title":"Eur. J. Obs. Gynecol. Reprod. Biology"},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b9","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1021\/acs.jcim.4c01544","article-title":"Neighborhood topology-aware knowledge graph learning and microbial preference inferring for drug-microbe association prediction","volume":"65","author":"Gu","year":"2025","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/prd.12331","article-title":"Current understanding of periodontal disease pathogenesis and targets for host-modulation therapy","volume":"84","author":"Hajishengallis","year":"2020","journal-title":"Periodontol. 2000"},{"key":"10.1016\/j.biosystems.2026.105729_b11","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"7","key":"10.1016\/j.biosystems.2026.105729_b12","first-page":"563","article-title":"Human gut microbiome: function matters","volume":"26","author":"Heintz-Buschart","year":"2018","journal-title":"TIM"},{"issue":"26","key":"10.1016\/j.biosystems.2026.105729_b13","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1056\/NEJM199512283332604","article-title":"Association between bacterial vaginosis and preterm delivery of a low-birth-weight infant","volume":"333","author":"Hillier","year":"1995","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.biosystems.2026.105729_b14","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3389\/fmicb.2017.00233","article-title":"PBHMDA: path-based human microbe-disease association prediction","volume":"8","author":"Huang","year":"2017","journal-title":"Front. Microbiol."},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b15","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1186\/s12967-017-1304-7","article-title":"Prediction of microbe\u2013disease association from the integration of neighbor and graph with collaborative recommendation model","volume":"15","author":"Huang","year":"2017","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.biosystems.2026.105729_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12866-018-1197-5","article-title":"Disbiome database: linking the microbiome to disease","volume":"18","author":"Janssens","year":"2018","journal-title":"BMC Microbiol."},{"key":"10.1016\/j.biosystems.2026.105729_b17","series-title":"Seminars in Nephrology","first-page":"104","article-title":"Diabetes and the gut microbiome","volume":"vol. 41","author":"Lau","year":"2021"},{"key":"10.1016\/j.biosystems.2026.105729_b18","doi-asserted-by":"crossref","first-page":"676","DOI":"10.3389\/fmicb.2019.00676","article-title":"A novel human microbe-disease association prediction method based on the bidirectional weighted network","volume":"10","author":"Li","year":"2019","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.biosystems.2026.105729_b19","article-title":"Exploring microbe-drug association prediction via multi-attribute dual-decoder graph autoencoder","author":"Liu","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b20","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12911-025-03093-6","article-title":"A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships","volume":"25","author":"Liu","year":"2025","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"6","key":"10.1016\/j.biosystems.2026.105729_b21","doi-asserted-by":"crossref","first-page":"3578","DOI":"10.1109\/TCBB.2021.3116318","article-title":"MGATMDA: Predicting microbe-disease associations via multi-component graph attention network","volume":"19","author":"Liu","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"10.1016\/j.biosystems.2026.105729_b22","series-title":"Kan: Kolmogorov-arnold networks","author":"Liu","year":"2024"},{"key":"10.1016\/j.biosystems.2026.105729_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2024.108320","article-title":"DMNAG: Prediction of disease-metabolite associations based on neighborhood aggregation graph transformer","volume":"115","author":"Lu","year":"2025","journal-title":"Comput. Biol. Chem."},{"issue":"24","key":"10.1016\/j.biosystems.2026.105729_b24","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1056\/NEJMra1600266","article-title":"The human intestinal microbiome in health and disease","volume":"375","author":"Lynch","year":"2016","journal-title":"N. Engl. J. Med."},{"issue":"24","key":"10.1016\/j.biosystems.2026.105729_b25","doi-asserted-by":"crossref","first-page":"5665","DOI":"10.1093\/bioinformatics\/btaa1080","article-title":"Ninimhmda: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human microbe\u2013disease association","volume":"36","author":"Ma","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1093\/bib\/bbw005","article-title":"An analysis of human microbe\u2013disease associations","volume":"18","author":"Ma","year":"2017","journal-title":"Brief. Bioinform."},{"issue":"11","key":"10.1016\/j.biosystems.2026.105729_b27","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"10.1016\/j.biosystems.2026.105729_b28","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1111\/odi.12472","article-title":"The microbiome\u2013systemic diseases connection","volume":"22","author":"van der Meulen","year":"2016","journal-title":"Oral Dis."},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1093\/epirev\/mxx006","article-title":"Periodontal disease, tooth loss, and cancer risk","volume":"39","author":"Michaud","year":"2017","journal-title":"Epidemiol. Rev."},{"issue":"2","key":"10.1016\/j.biosystems.2026.105729_b30","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1128\/CMR.00075-15","article-title":"The human microbiome during bacterial vaginosis","volume":"29","author":"Onderdonk","year":"2016","journal-title":"Clin. Microbiol. Rev."},{"key":"10.1016\/j.biosystems.2026.105729_b31","article-title":"Multi-view feature aggregation for predicting microbe-disease association","author":"Peng","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biology Bioinform."},{"key":"10.1016\/j.biosystems.2026.105729_b32","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2020.592430","article-title":"RNMFMDA: a microbe-disease association identification method based on reliable negative sample selection and logistic matrix factorization with neighborhood regularization","volume":"11","author":"Peng","year":"2020","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.biosystems.2026.105729_b33","series-title":"A theory of networks for appxoimation and learning","author":"Poggio","year":"1989"},{"issue":"D1","key":"10.1016\/j.biosystems.2026.105729_b34","doi-asserted-by":"crossref","first-page":"D1255","DOI":"10.1093\/nar\/gkab1063","article-title":"The human disease ontology 2022 update","volume":"50","author":"Schriml","year":"2022","journal-title":"Nucleic Acids Res."},{"issue":"6536","key":"10.1016\/j.biosystems.2026.105729_b35","doi-asserted-by":"crossref","first-page":"eabc4552","DOI":"10.1126\/science.abc4552","article-title":"The microbiome and human cancer","volume":"371","author":"Sepich-Poore","year":"2021","journal-title":"Science"},{"key":"10.1016\/j.biosystems.2026.105729_b36","doi-asserted-by":"crossref","DOI":"10.3389\/fcimb.2018.00424","article-title":"MDAD: A special resource for microbe-drug associations","author":"Sun","year":"2018","journal-title":"Front. Cell. Infect. Microbiol."},{"issue":"1","key":"10.1016\/j.biosystems.2026.105729_b37","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.phrs.2012.11.006","article-title":"The oral microbiome in health and disease","volume":"69","author":"Wade","year":"2013","journal-title":"Pharmacol. Res."},{"issue":"4","key":"10.1016\/j.biosystems.2026.105729_b38","doi-asserted-by":"crossref","first-page":"bbad255","DOI":"10.1093\/bib\/bbad255","article-title":"Predicting potential microbe\u2013disease associations based on multi-source features and deep learning","volume":"24","author":"Wang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.biosystems.2026.105729_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2025.108581","article-title":"Enhancing microbe-disease association prediction via multi-view graph convolution and latent feature learning","author":"Wang","year":"2025","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.biosystems.2026.105729_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110301","article-title":"Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder","volume":"147","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"18","key":"10.1016\/j.biosystems.2026.105729_b41","doi-asserted-by":"crossref","DOI":"10.1111\/jcmm.70071","article-title":"ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations","volume":"28","author":"Wen","year":"2024","journal-title":"J. Cell. Mol. Med."},{"key":"10.1016\/j.biosystems.2026.105729_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108039","article-title":"DMCFMDA: A dual-channel multi-source cross-modal fusion network with contrastive learning for microbe\u2013disease prediction","volume":"110","author":"Wu","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"issue":"6","key":"10.1016\/j.biosystems.2026.105729_b43","doi-asserted-by":"crossref","first-page":"5079","DOI":"10.1109\/TCYB.2020.3026652","article-title":"NCPLP: a novel approach for predicting microbe-associated diseases with network consistency projection and label propagation","volume":"52","author":"Yin","year":"2020","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"10.1016\/j.biosystems.2026.105729_b44","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1093\/bioinformatics\/btu684","article-title":"DOSE: an r\/bioconductor package for disease ontology semantic and enrichment analysis","volume":"31","author":"Yu","year":"2014","journal-title":"Bioinformatics"},{"key":"10.1016\/j.biosystems.2026.105729_b45","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.biopha.2018.01.146","article-title":"Human oral microbiota and its modulation for oral health","volume":"99","author":"Zhang","year":"2018","journal-title":"Biomed. Pharmacother."},{"issue":"3","key":"10.1016\/j.biosystems.2026.105729_b46","doi-asserted-by":"crossref","first-page":"bbaa158","DOI":"10.1093\/bib\/bbaa158","article-title":"Microbes and complex diseases: from experimental results to computational models","volume":"22","author":"Zhao","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.biosystems.2026.105729_b47","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad483","article-title":"Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs","author":"Zhou","year":"2023","journal-title":"Brief. Bioinform."}],"container-title":["BioSystems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0303264726000390?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0303264726000390?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:55:13Z","timestamp":1777550113000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0303264726000390"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":47,"alternative-id":["S0303264726000390"],"URL":"https:\/\/doi.org\/10.1016\/j.biosystems.2026.105729","relation":{},"ISSN":["0303-2647"],"issn-type":[{"value":"0303-2647","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KMGTMDA: KAN-driven multiscale graph neural network and context-enhanced prediction for human microbe\u2013disease associations","name":"articletitle","label":"Article Title"},{"value":"BioSystems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.biosystems.2026.105729","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105729"}}