{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:38:33Z","timestamp":1769837913132,"version":"3.49.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"KU Leuven Internal","award":["CELSA\/17\/032"],"award-info":[{"award-number":["CELSA\/17\/032"]}]},{"name":"KU Leuven Internal","award":["KUL CoE PFV\/10\/016"],"award-info":[{"award-number":["KUL CoE PFV\/10\/016"]}]},{"name":"Imec ICON GAP"},{"DOI":"10.13039\/501100011878","name":"Flemish Government","doi-asserted-by":"crossref","award":["IWT 150865"],"award-info":[{"award-number":["IWT 150865"]}],"id":[{"id":"10.13039\/501100011878","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100011878","name":"Flemish Government","doi-asserted-by":"crossref","award":["FWO 06260"],"award-info":[{"award-number":["FWO 06260"]}],"id":[{"id":"10.13039\/501100011878","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004727","name":"VIB","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004727","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ELIXIR Flanders Bioinformatics Infrastructure for Sustainable Agriculture"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Bayesian data fusion method is implemented as a Python\/C++\u2009package: https:\/\/github.com\/jaak-s\/macau. It is also available as a Julia package: https:\/\/github.com\/jaak-s\/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https:\/\/owncloud.esat.kuleuven.be\/index.php\/s\/UGb89WfkZwMYoTn.<\/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\/bty289","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T19:11:48Z","timestamp":1523905908000},"page":"i447-i456","source":"Crossref","is-referenced-by-count":35,"title":["Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information"],"prefix":"10.1093","volume":"34","author":[{"given":"Pooya","family":"Zakeri","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium"}]},{"given":"Jaak","family":"Simm","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium"}]},{"given":"Adam","family":"Arany","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium"}]},{"given":"Sarah","family":"ElShal","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium"}]},{"given":"Yves","family":"Moreau","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven and imec, Kapeldreef Leuven, Belgium"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051605023784400_bty289-B1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1038\/nbt1203","article-title":"Gene prioritization through genomic data fusion","volume":"24","author":"Aerts","year":"2006","journal-title":"Nat. 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