{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:13:24Z","timestamp":1780589604314,"version":"3.54.1"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T00:00:00Z","timestamp":1551484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["CSC"],"award-info":[{"award-number":["CSC"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772552"],"award-info":[{"award-number":["61772552"]}],"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":["61571052"],"award-info":[{"award-number":["61571052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Foundation of Wuhan Institute of Technology","award":["K201746"],"award-info":[{"award-number":["K201746"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease\u2013gene associations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a method to predict disease\u2013gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease\u2013gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease\u2013gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease\u2013gene pairs also demonstrates the ability of dgMDL in predicting new disease\u2013gene associations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Prediction results and a reference implementation of dgMDL in Python is available on https:\/\/github.com\/luoping1004\/dgMDL.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz155","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T12:45:02Z","timestamp":1551271502000},"page":"3735-3742","source":"Crossref","is-referenced-by-count":56,"title":["Enhancing the prediction of disease\u2013gene associations with multimodal deep learning"],"prefix":"10.1093","volume":"35","author":[{"given":"Ping","family":"Luo","sequence":"first","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan , Saskatoon, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan , Saskatoon, Canada"},{"name":"School of Mathematics and Physics, Wuhan Institute of Technology , Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Ping","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Information, Beijing Wuzi University , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, University of Saskatchewan , Saskatoon, Canada"},{"name":"Department of Mechanical Engineering, University of Saskatchewan , Saskatoon, Canada"},{"name":"Department of Computer Science, University of Saskatchewan , Saskatoon, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,3,2]]},"reference":[{"key":"2023013108154428600_btz155-B1","doi-asserted-by":"crossref","first-page":"42.","DOI":"10.1186\/1750-1172-7-42","article-title":"Complement factor i deficiency: a not so rare immune defect. characterization of new mutations and the first large gene deletion","volume":"7","author":"Alba-Dom\u00ednguez","year":"2012","journal-title":"Orphanet J. 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