{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:05:43Z","timestamp":1780448743074,"version":"3.54.1"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T00:00:00Z","timestamp":1479859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS 1149697"],"award-info":[{"award-number":["IIS 1149697"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAPES Foundation, Ministry of Education of Brazil","award":["BEX 13250\/13-2"],"award-info":[{"award-number":["BEX 13250\/13-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype\u2013gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term\u2013gene associations and HPO phenotype\u2013gene associations for all the genes in a protein\u2013protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein\u2013protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term\u2013gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>Source code is available at http:\/\/compbio.cs.umn.edu\/ontophenome.<\/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\/btw649","type":"journal-article","created":{"date-parts":[[2016,10,11]],"date-time":"2016-10-11T19:05:32Z","timestamp":1476212732000},"page":"529-536","source":"Crossref","is-referenced-by-count":37,"title":["Transfer learning across ontologies for phenome\u2013genome association prediction"],"prefix":"10.1093","volume":"33","author":[{"given":"Raphael","family":"Petegrosso","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunho","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae Hyun","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Kuang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,11,23]]},"reference":[{"key":"2023020204414605000_btw649-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. 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