{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:08:21Z","timestamp":1772114901445,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Youth Innovation Talent Research Project of the Basic Scientifc Research Business Fund for Provincial Universities in Heilongjiang Province","award":["145209206"],"award-info":[{"award-number":["145209206"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-025-02930-4","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T12:11:22Z","timestamp":1772107882000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting microbe-disease association based on multi-kernel autoencoder and singular value decomposition"],"prefix":"10.1007","volume":"17","author":[{"given":"Xiaoxin","family":"Du","sequence":"first","affiliation":[]},{"given":"Lisen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Zhenfei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianfei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"issue":"D1","key":"2930_CR1","doi-asserted-by":"publisher","first-page":"D554","DOI":"10.1093\/nar\/gkz843","volume":"48","author":"L Cheng","year":"2020","unstructured":"Cheng L, Qi C, Zhuang H et al (2020) gutmdisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions. Nucleic Acids Res 48(D1):D554\u2013D560","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"2930_CR2","doi-asserted-by":"publisher","first-page":"849","DOI":"10.7150\/ijbs.24539","volume":"14","author":"C Wu","year":"2018","unstructured":"Wu C, Gao R, Zhang D et al (2018) Prwhmda: human microbe-disease association prediction by random walk on the heterogeneous network with PSO. Int J Biol Sci 14(8):849","journal-title":"Int J Biol Sci"},{"key":"2930_CR3","doi-asserted-by":"crossref","unstructured":"Wen Z, Yan C, Duan G, et\u00a0al (2021) A survey on predicting microbe-disease associations: biological data and computational methods. Brief Bioinform 22(3):bbaa157","DOI":"10.1093\/bib\/bbaa157"},{"issue":"8","key":"2930_CR4","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1038\/nrgastro.2015.114","volume":"12","author":"E Holmes","year":"2015","unstructured":"Holmes E, Wijeyesekera A, Taylor-Robinson SD et al (2015) The promise of metabolic phenotyping in gastroenterology and hepatology. Nat Rev Gastroenterol Hepatol 12(8):458\u2013471","journal-title":"Nat Rev Gastroenterol Hepatol"},{"issue":"4","key":"2930_CR5","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1016\/j.neuroscience.2010.08.005","volume":"170","author":"L Desbonnet","year":"2010","unstructured":"Desbonnet L, Garrett L, Clarke G et al (2010) Effects of the probiotic bifidobacterium infantis in the maternal separation model of depression. Neuroscience 170(4):1179\u20131188","journal-title":"Neuroscience"},{"key":"2930_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13099-018-0276-3","volume":"10","author":"MI El Mouzan","year":"2018","unstructured":"El Mouzan MI, Winter HS, Assiri AA et al (2018) Microbiota profile in new-onset pediatric Crohn\u2019s disease: data from a non-western population. Gut Pathogens 10:1\u201310","journal-title":"Gut Pathogens"},{"key":"2930_CR7","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neucom.2018.09.054","volume":"323","author":"C Fan","year":"2019","unstructured":"Fan C, Lei X, Guo L et al (2019) Predicting the associations between microbes and diseases by integrating multiple data sources and path-based hetesim scores. Neurocomputing 323:76\u201385","journal-title":"Neurocomputing"},{"key":"2930_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3066-0","volume":"20","author":"Y Long","year":"2019","unstructured":"Long Y, Luo J (2019) Wmghmda: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network. BMC Bioinform 20:1\u201318","journal-title":"BMC Bioinform"},{"issue":"4","key":"2930_CR9","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1109\/TCBB.2018.2883041","volume":"17","author":"J Luo","year":"2018","unstructured":"Luo J, Long Y (2018) Ntshmda: prediction of human microbe-disease association based on random walk by integrating network topological similarity. IEEE\/ACM Trans Comput Biol Bioinf 17(4):1341\u20131351","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"1","key":"2930_CR10","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1186\/s12859-022-04961-y","volume":"23","author":"J Guan","year":"2022","unstructured":"Guan J, Zhang ZG, Liu Y et al (2022) A novel bi-directional heterogeneous network selection method for disease and microbial association prediction. BMC Bioinform 23(1):483","journal-title":"BMC Bioinform"},{"key":"2930_CR11","doi-asserted-by":"publisher","first-page":"31341","DOI":"10.1109\/ACCESS.2020.2972283","volume":"8","author":"L Peng","year":"2020","unstructured":"Peng L, Zhou D, Liu W et al (2020) Prioritizing human microbe-disease associations utilizing a node-information-based link propagation method. IEEE Access 8:31341\u201331349","journal-title":"IEEE Access"},{"key":"2930_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2020.592430","volume":"11","author":"L Peng","year":"2020","unstructured":"Peng L, Shen L, Liao L et al (2020) Rnmfmda: a microbe-disease association identification method based on reliable negative sample selection and logistic matrix factorization with neighborhood regularization. Front Microbiol 11:592430","journal-title":"Front Microbiol"},{"issue":"2","key":"2930_CR13","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TCBB.2019.2926716","volume":"18","author":"C Yan","year":"2019","unstructured":"Yan C, Duan G, Wu FX et al (2019) Mchmda: predicting microbe-disease associations based on similarities and low-rank matrix completion. IEEE\/ACM Trans Comput Biol Bioinf 18(2):611\u2013620","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2930_CR14","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.csbj.2022.12.053","volume":"21","author":"H Liu","year":"2023","unstructured":"Liu H, Bing P, Zhang M et al (2023) Mnnmda: predicting human microbe-disease association via a method to minimize matrix nuclear norm. Comput Struct Biotechnol J 21:1414\u20131423","journal-title":"Comput Struct Biotechnol J"},{"key":"2930_CR15","doi-asserted-by":"crossref","unstructured":"Chen X, Li TH, Zhao Y, et\u00a0al (2021) Deep-belief network for predicting potential mirna-disease associations. Brief Bioinform 22(3):bbaa186","DOI":"10.1093\/bib\/bbaa186"},{"issue":"1","key":"2930_CR16","first-page":"136","volume":"13","author":"J Ha","year":"2025","unstructured":"Ha J (2025) Graph convolutional network with neural collaborative filtering for predicting mirna-disease association. Biomed 13(1):136","journal-title":"Biomed"},{"issue":"3","key":"2930_CR17","first-page":"536","volume":"13","author":"J Ha","year":"2025","unstructured":"Ha J (2025) Deepwalk-based graph embeddings for mirna-disease association prediction using deep neural network. Biomed 13(3):536","journal-title":"Biomed"},{"key":"2930_CR18","doi-asserted-by":"crossref","unstructured":"Long Y, Luo J, Zhang Y, et\u00a0al (2021) Predicting human microbe\u2013disease associations via graph attention networks with inductive matrix completion. Brief Bioinform 22(3):bbaa146","DOI":"10.1093\/bib\/bbaa146"},{"key":"2930_CR19","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2020.09.094","volume":"469","author":"W Lan","year":"2022","unstructured":"Lan W, Wu X, Chen Q et al (2022) Ganlda: graph attention network for lncrna-disease associations prediction. Neurocomputing 469:384\u2013393","journal-title":"Neurocomputing"},{"issue":"2","key":"2930_CR20","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1049\/cje.2020.00.212","volume":"31","author":"Y Wang","year":"2022","unstructured":"Wang Y, Lei X, Pan Y (2022) Predicting microbe-disease association based on heterogeneous network and global graph feature learning. Chin J Electron 31(2):345\u2013353","journal-title":"Chin J Electron"},{"issue":"2","key":"2930_CR21","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TCBB.2022.3184362","volume":"20","author":"C Jiang","year":"2022","unstructured":"Jiang C, Tang M, Jin S et al (2022) Kgnmda: a knowledge graph neural network method for predicting microbe-disease associations. IEEE\/ACM Trans Comput Biol Bioinf 20(2):1147\u20131155","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2930_CR22","doi-asserted-by":"crossref","unstructured":"Lan W, Dong Y, Chen Q, et\u00a0al (2022a) Kgancda: predicting circrna-disease associations based on knowledge graph attention network. Briefings Bioinform 23(1):bbab494","DOI":"10.1093\/bib\/bbab494"},{"issue":"1","key":"2930_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3698192","volume":"43","author":"W Lan","year":"2024","unstructured":"Lan W, Zhou G, Chen Q et al (2024) Contrastive clustering learning for multi-behavior recommendation. ACM Trans Inf Syst 43(1):1\u201323","journal-title":"ACM Trans Inf Syst"},{"key":"2930_CR24","doi-asserted-by":"publisher","first-page":"1207209","DOI":"10.3389\/fmicb.2023.1207209","volume":"14","author":"F Wang","year":"2023","unstructured":"Wang F, Yang H, Wu Y et al (2023) Saelgmda: identifying human microbe-disease associations based on sparse autoencoder and lightgbm. Front Microbiol 14:1207209","journal-title":"Front Microbiol"},{"key":"2930_CR25","doi-asserted-by":"crossref","unstructured":"Wang L, Wang Y, Xuan C, et\u00a0al (2023b) Predicting potential microbe\u2013disease associations based on multi-source features and deep learning. Brief Bioinform 24(4):bbad255","DOI":"10.1093\/bib\/bbad255"},{"issue":"3","key":"2930_CR26","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1109\/TCBB.2020.3034910","volume":"19","author":"W Lan","year":"2020","unstructured":"Lan W, Lai D, Chen Q et al (2020) Ldicdl: Lncrna-disease association identification based on collaborative deep learning. IEEE\/ACM Trans Comput Biol Bioinf 19(3):1715\u20131723","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2930_CR27","doi-asserted-by":"publisher","first-page":"1244527","DOI":"10.3389\/fmicb.2023.1244527","volume":"14","author":"L Peng","year":"2023","unstructured":"Peng L, Huang L, Tian G et al (2023) Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network. Front Microbiol 14:1244527","journal-title":"Front Microbiol"},{"issue":"1","key":"2930_CR28","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1093\/bib\/bbw005","volume":"18","author":"W Ma","year":"2017","unstructured":"Ma W, Zhang L, Zeng P et al (2017) An analysis of human microbe-disease associations. Brief Bioinform 18(1):85\u201397","journal-title":"Brief Bioinform"},{"key":"2930_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12866-018-1197-5","volume":"18","author":"Y Janssens","year":"2018","unstructured":"Janssens Y, Nielandt J, Bronselaer A et al (2018) Disbiome database: linking the microbiome to disease. BMC Microbiol 18:1\u20136","journal-title":"BMC Microbiol"},{"issue":"D1","key":"2930_CR30","doi-asserted-by":"publisher","first-page":"D1328","DOI":"10.1093\/nar\/gkaa902","volume":"49","author":"G Skoufos","year":"2021","unstructured":"Skoufos G, Kardaras FS, Alexiou A et al (2021) Peryton: a manual collection of experimentally supported microbe-disease associations. Nucleic Acids Res 49(D1):D1328\u2013D1333","journal-title":"Nucleic Acids Res"},{"key":"2930_CR31","unstructured":"Schriml L (2016) Human disease ontology"},{"issue":"13","key":"2930_CR32","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","volume":"26","author":"D Wang","year":"2010","unstructured":"Wang D, Wang J, Lu M et al (2010) Inferring the human microrna functional similarity and functional network based on microrna-associated diseases. Bioinformatics 26(13):1644\u20131650","journal-title":"Bioinformatics"},{"key":"2930_CR33","doi-asserted-by":"crossref","unstructured":"Li J, Gong B, Chen X, et al (2011) Dosim: an R package for similarity between diseases based on dis ease ontology","DOI":"10.1186\/1471-2105-12-266"},{"issue":"1","key":"2930_CR34","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1109\/TCBB.2022.3146176","volume":"20","author":"JX Liu","year":"2022","unstructured":"Liu JX, Yin MM, Gao YL et al (2022) Msf-lrr: multi-similarity information fusion through low-rank representation to predict disease-associated microbes. IEEE\/ACM Trans Comput Biol Bioinf 20(1):534\u2013543","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"21","key":"2930_CR35","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","volume":"27","author":"T Van Laarhoven","year":"2011","unstructured":"Van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21):3036\u20133043","journal-title":"Bioinformatics"},{"key":"2930_CR36","doi-asserted-by":"crossref","unstructured":"Qian Y, Ding Y, Zou Q, et\u00a0al (2022) Identification of drug-side effect association via restricted boltzmann machines with penalized term. Brief Bioinform 23(6):bbac458","DOI":"10.1093\/bib\/bbac458"},{"key":"2930_CR37","doi-asserted-by":"crossref","unstructured":"Liu W, Lin H, Huang L, et\u00a0al (2022b) Identification of mirna\u2013disease associations via deep forest ensemble learning based on autoencoder. Brief Bioinform 23(3):bbac104","DOI":"10.1093\/bib\/bbac104"},{"issue":"1","key":"2930_CR38","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/s12859-022-04694-y","volume":"23","author":"L Deng","year":"2022","unstructured":"Deng L, Liu Z, Qian Y et al (2022) Predicting circrna-drug sensitivity associations via graph attention auto-encoder. BMC Bioinform 23(1):160","journal-title":"BMC Bioinform"},{"issue":"2","key":"2930_CR39","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TCBB.2022.3191972","volume":"20","author":"J Ha","year":"2022","unstructured":"Ha J, Park S (2022) Ncmd: Node2vec-based neural collaborative filtering for predicting mirna-disease association. IEEE\/ACM Trans Comput Biol Bioinf 20(2):1257\u20131268","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"6","key":"2930_CR40","doi-asserted-by":"publisher","first-page":"885","DOI":"10.3390\/jpm12060885","volume":"12","author":"J Ha","year":"2022","unstructured":"Ha J (2022) Mdmf: Predicting mirna-disease association based on matrix factorization with disease similarity constraint. J Pers Med 12(6):885","journal-title":"J Pers Med"},{"key":"2930_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110295","volume":"263","author":"J Ha","year":"2023","unstructured":"Ha J (2023) Smap: Similarity-based matrix factorization framework for inferring mirna-disease association. Knowl-Based Syst 263:110295","journal-title":"Knowl-Based Syst"},{"issue":"22","key":"2930_CR42","doi-asserted-by":"publisher","first-page":"8641","DOI":"10.1021\/acs.jcim.4c01589","volume":"64","author":"T Liu","year":"2024","unstructured":"Liu T, Wang S, Zhang Y et al (2024) Tiwmflp: two-tier interactive weighted matrix factorization and label propagation based on similarity matrix fusion for drug-disease association prediction. J Chem Inf Model 64(22):8641\u20138654","journal-title":"J Chem Inf Model"},{"key":"2930_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103358","volume":"102","author":"J Ha","year":"2020","unstructured":"Ha J, Park C, Park C et al (2020) Imipmf: Inferring mirna-disease interactions using probabilistic matrix factorization. J Biomed Inform 102:103358","journal-title":"J Biomed Inform"},{"key":"2930_CR44","doi-asserted-by":"publisher","first-page":"78847","DOI":"10.1109\/ACCESS.2021.3084148","volume":"9","author":"J Ha","year":"2021","unstructured":"Ha J, Park C (2021) Mlmd: Metric learning for predicting mirna-disease associations. Ieee Access 9:78847\u201378858","journal-title":"Ieee Access"},{"issue":"912","key":"2930_CR45","first-page":"44","volume":"907","author":"H Abdi","year":"2007","unstructured":"Abdi H (2007) Singular Value Decomposition (SVD) and generalized singular value decomposition. Encycl Meas Stat 907(912):44","journal-title":"Encycl Meas Stat"},{"key":"2930_CR46","doi-asserted-by":"crossref","unstructured":"ZhouZH F (2017) Deepforest: Towards an alternative to deep neural networks. Proceedings of the Twenty? In: 6th International Joint conference on Artificial Intelligence, pp 3553r3559","DOI":"10.24963\/ijcai.2017\/497"},{"key":"2930_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114876","volume":"176","author":"B Yu","year":"2021","unstructured":"Yu B, Chen C, Wang X et al (2021) Prediction of protein-protein interactions based on elastic net and deep forest. Expert Syst Appl 176:114876","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2930_CR48","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1093\/bib\/bbz080","volume":"21","author":"X Zeng","year":"2020","unstructured":"Zeng X, Zhong Y, Lin W et al (2020) Predicting disease-associated circular rnas using deep forests combined with positive-unlabeled learning methods. Brief Bioinform 21(4):1425\u20131436","journal-title":"Brief Bioinform"},{"key":"2930_CR49","doi-asserted-by":"crossref","unstructured":"Zhu YH, Hu J, Ge F, et\u00a0al (2021) Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features. Brief Bioinform 22(3):bbaa076","DOI":"10.1093\/bib\/bbaa076"},{"key":"2930_CR50","doi-asserted-by":"crossref","unstructured":"Zhou YZ, Gao Y, Zheng YY (2011) Prediction of protein-protein interactions using local description of amino acid sequence pp 254\u2013262","DOI":"10.1007\/978-3-642-22456-0_37"},{"issue":"1","key":"2930_CR51","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Mach learn Random forests 45(1):5\u201332","journal-title":"Mach learn Random forests"},{"key":"2930_CR52","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3\u201342","journal-title":"Mach Learn"},{"key":"2930_CR53","doi-asserted-by":"crossref","unstructured":"Zhou ZH (2012) Ensemble methods: foundations and algorithms","DOI":"10.1201\/b12207"},{"key":"2930_CR54","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"2930_CR55","unstructured":"Ke G, Meng Q, Finley T, et\u00a0al (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30"},{"key":"2930_CR56","doi-asserted-by":"crossref","unstructured":"Cortes C (1995) Support-vector networks. Mach Learn","DOI":"10.1007\/BF00994018"},{"issue":"6","key":"2930_CR57","doi-asserted-by":"publisher","first-page":"3530","DOI":"10.1109\/TCBB.2021.3111607","volume":"19","author":"W Lan","year":"2021","unstructured":"Lan W, Dong Y, Chen Q et al (2021) Ignscda: predicting circrna-disease associations based on improved graph convolutional network and negative sampling. IEEE\/ACM Trans Comput Biol Bioinf 19(6):3530\u20133538","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2930_CR58","doi-asserted-by":"crossref","unstructured":"Lan W, Li C, Chen Q, et\u00a0al (2024a) Lgcda: Predicting circrna-disease association based on fusion of local and global features. IEEE\/ACM Trans Comput Biol Bioinform","DOI":"10.1109\/TCBB.2024.3387913"},{"issue":"24","key":"2930_CR59","doi-asserted-by":"publisher","first-page":"3984","DOI":"10.3390\/cells11243984","volume":"11","author":"S Wang","year":"2022","unstructured":"Wang S, Lin B, Zhang Y et al (2022) Sgaemda: predicting mirna-disease associations based on stacked graph autoencoder. Cells 11(24):3984","journal-title":"Cells"},{"key":"2930_CR60","doi-asserted-by":"crossref","unstructured":"Chen H, Chen K (2025) Ensemble learning based on matrix completion improves microbe-disease association prediction. Brief Bioinform 26(2):bbaf075","DOI":"10.1093\/bib\/bbaf075"},{"key":"2930_CR61","doi-asserted-by":"publisher","first-page":"1578140","DOI":"10.3389\/fphar.2025.1578140","volume":"16","author":"Q Ye","year":"2025","unstructured":"Ye Q, Sun Y (2025) Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery. Front Pharmacol 16:1578140","journal-title":"Front Pharmacol"},{"key":"2930_CR62","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.3098","volume":"11","author":"R Zhu","year":"2025","unstructured":"Zhu R, Wang Y, Shang J et al (2025) Optimizing transformer-based prediction of human microbe-disease associations through integrated loss strategies. PeerJ Comput Sci 11:e3098","journal-title":"PeerJ Comput Sci"},{"issue":"7","key":"2930_CR63","doi-asserted-by":"publisher","first-page":"663","DOI":"10.2174\/0115748936270441231116093650","volume":"19","author":"J Xing","year":"2024","unstructured":"Xing J, Shi Y, Su X et al (2024) Discovering microbe-disease associations with weighted graph convolution networks and taxonomy common tree. Curr Bioinform 19(7):663\u2013673","journal-title":"Curr Bioinform"},{"key":"2930_CR64","doi-asserted-by":"crossref","unstructured":"Chen J, Tao R, Qiu Y, et\u00a0al (2024) Cmfhmda: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization. Brief Bioinform 25(6):bbae481","DOI":"10.1093\/bib\/bbae481"},{"issue":"1","key":"2930_CR65","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1186\/s12915-023-01796-8","volume":"21","author":"H Zhu","year":"2023","unstructured":"Zhu H, Hao H, Yu L (2023) Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding wasserstein distance. BMC Biol 21(1):294","journal-title":"BMC Biol"},{"key":"2930_CR66","doi-asserted-by":"publisher","first-page":"1170559","DOI":"10.3389\/fmicb.2023.1170559","volume":"14","author":"Q Liao","year":"2023","unstructured":"Liao Q, Ye Y, Li Z et al (2023) Prediction of mirna-disease associations in microbes based on graph convolutional networks and autoencoders. Front Microbiol 14:1170559","journal-title":"Front Microbiol"},{"issue":"5","key":"2930_CR67","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1038\/s41574-022-00794-0","volume":"19","author":"M Van Hul","year":"2023","unstructured":"Van Hul M, Cani PD (2023) The gut microbiota in obesity and weight management: microbes as friends or foe? Nat Rev Endocrinol 19(5):258\u2013271","journal-title":"Nat Rev Endocrinol"},{"issue":"2","key":"2930_CR68","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.cmet.2022.12.013","volume":"35","author":"T Takeuchi","year":"2023","unstructured":"Takeuchi T, Kameyama K, Miyauchi E et al (2023) Fatty acid overproduction by gut commensal microbiota exacerbates obesity. Cell Metab 35(2):361\u2013375","journal-title":"Cell Metab"},{"key":"2930_CR69","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.8317","volume":"8","author":"X Chen","year":"2020","unstructured":"Chen X, Sun H, Jiang F et al (2020) Alteration of the gut microbiota associated with childhood obesity by 16s rrna gene sequencing. PeerJ 8:e8317","journal-title":"PeerJ"},{"key":"2930_CR70","doi-asserted-by":"crossref","unstructured":"Butt\u00f3 LF, Schaubeck M, Haller D (2015) Mechanisms of microbe\u2013host interaction in crohn\u2019s disease: dysbiosis vs. pathobiont selection. Front Immunol 6:555","DOI":"10.3389\/fimmu.2015.00555"},{"issue":"2","key":"2930_CR71","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1086\/510385","volume":"44","author":"PB Eckburg","year":"2007","unstructured":"Eckburg PB, Relman DA (2007) The role of microbes in Crohn\u2019s disease. Clin Infect Dis 44(2):256\u2013262","journal-title":"Clin Infect Dis"},{"issue":"3","key":"2930_CR72","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1093\/ecco-jcc\/jjv209","volume":"10","author":"G Liguori","year":"2016","unstructured":"Liguori G, Lamas B, Richard ML et al (2016) Fungal dysbiosis in mucosa-associated microbiota of Crohn\u2019s disease patients. J Crohns Colitis 10(3):296\u2013305","journal-title":"J Crohns Colitis"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02930-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02930-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02930-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:02:50Z","timestamp":1772110970000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02930-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":72,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2930"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02930-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,26]]},"assertion":[{"value":"21 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors consent to the publication of this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Datasets and codes are publicly available at\n                      \n                      .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"All data used in this study are publicly available and have been deposited in the GitHub repository: https:\/\/github.com\/senliyang\/CFAESVD.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}],"article-number":"140"}}