{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:50:41Z","timestamp":1765356641234,"version":"3.37.3"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000275","name":"Leverhulme Trust","doi-asserted-by":"publisher","award":["RPG-2016-015"],"award-info":[{"award-number":["RPG-2016-015"]}],"id":[{"id":"10.13039\/501100000275","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["208375\/Z\/17\/Z"],"award-info":[{"award-number":["208375\/Z\/17\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/R014949\/1"],"award-info":[{"award-number":["BB\/R014949\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pre-compiled list of human genes associated with age-related diseases and apply a novel Deep Neural Network (DNN) method to find associations between gene descriptors (e.g. Gene Ontology terms, protein\u2013protein interaction data and biological pathway information) and age-related diseases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The novelty of our new DNN method is its modular architecture, which has the capability of combining several sources of biological data to predict which ageing-related diseases a gene is associated with (if any). Our DNN method achieves better predictive performance than standard DNN approaches, a Gradient Boosted Tree classifier (a strong baseline method) and a Logistic Regression classifier. Given the DNN model produced by our method, we use two approaches to identify human genes that are not known to be associated with age-related diseases according to our dataset. First, we investigate genes that are close to other disease-associated genes in a complex multi-dimensional feature space learned by the DNN algorithm. Second, using the class label probabilities output by our DNN approach, we identify genes with a high probability of being associated with age-related diseases according to the model. We provide evidence of these putative associations retrieved from the DNN model with literature support.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and datasets can be found at: https:\/\/github.com\/fabiofabris\/Bioinfo2019.<\/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\/btz887","type":"journal-article","created":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T20:11:16Z","timestamp":1576267876000},"page":"2202-2208","source":"Crossref","is-referenced-by-count":19,"title":["Using deep learning to associate human genes with age-related diseases"],"prefix":"10.1093","volume":"36","author":[{"given":"Fabio","family":"Fabris","sequence":"first","affiliation":[{"name":"School of Computing, University of Kent , Canterbury, Kent CT2 7NF, UK"}]},{"given":"Daniel","family":"Palmer","sequence":"additional","affiliation":[{"name":"Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool , Liverpool L7 8TX, UK"}]},{"given":"Khalid M","family":"Salama","sequence":"additional","affiliation":[{"name":"School of Computing, University of Kent , Canterbury, Kent CT2 7NF, UK"}]},{"given":"Jo\u00e3o Pedro","family":"de Magalh\u00e3es","sequence":"additional","affiliation":[{"name":"Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool , Liverpool L7 8TX, UK"}]},{"given":"Alex A","family":"Freitas","sequence":"additional","affiliation":[{"name":"School of Computing, University of Kent , Canterbury, Kent CT2 7NF, UK"}]}],"member":"286","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"2023062300072107700_btz887-B1","doi-asserted-by":"crossref","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. 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