{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:26:02Z","timestamp":1765887962435,"version":"3.37.3"},"reference-count":72,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"BBSRC","doi-asserted-by":"publisher","award":["BB\/F017464\/1"],"award-info":[{"award-number":["BB\/F017464\/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":[[2018,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of\u2009\u22640.97, outperforming go2ppi\u2014a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/ima23\/maxent-ppi<\/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\/btx803","type":"journal-article","created":{"date-parts":[[2018,1,29]],"date-time":"2018-01-29T20:12:57Z","timestamp":1517256777000},"page":"1884-1892","source":"Crossref","is-referenced-by-count":17,"title":["Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation"],"prefix":"10.1093","volume":"34","author":[{"given":"Irina M","family":"Armean","sequence":"first","affiliation":[{"name":"Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn S","family":"Lilley","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew W B","family":"Trotter","sequence":"additional","affiliation":[{"name":"Celegene Institute for Translational Research Europe (CITRE), Sevilla, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas C V","family":"Pilkington","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean B","family":"Holden","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"key":"2023012713555121100_btx803-B1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/S0092-8674(00)80922-8","volume":"92","author":"Alberts","year":"1998","journal-title":"Cell"},{"key":"2023012713555121100_btx803-B2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1038\/nbt0210-128","volume":"28","author":"Alterovitz","year":"2010","journal-title":"Nat. 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