{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:22:57Z","timestamp":1790140977784,"version":"4.0.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Council KU Leuven","award":["C14\/18\/092"],"award-info":[{"award-number":["C14\/18\/092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The prediction of reliable Drug\u2013Target Interactions (DTIs) is a key task in computer-aided drug design and repurposing. Here, we present a new approach based on data fusion for DTI prediction built on top of the NXTfusion library, which generalizes the Matrix Factorization paradigm by extending it to the nonlinear inference over Entity\u2013Relation graphs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We benchmarked our approach on five datasets and we compared our models against state-of-the-art methods. Our models outperform most of the existing methods and, simultaneously, retain the flexibility to predict both DTIs as binary classification and regression of the real-valued drug\u2013target affinity, competing with models built explicitly for each task. Moreover, our findings suggest that the validation of DTI methods should be stricter than what has been proposed in some previous studies, focusing more on mimicking real-life DTI settings where predictions for previously unseen drugs, proteins, and drug\u2013protein pairs are needed. These settings are exactly the context in which the benefit of integrating heterogeneous information with our Entity\u2013Relation data fusion approach is the most evident.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All software and data are available at https:\/\/github.com\/eugeniomazzone\/CPI-NXTFusion and https:\/\/pypi.org\/project\/NXTfusion\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad348","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T13:13:05Z","timestamp":1685538785000},"source":"Crossref","is-referenced-by-count":9,"title":["Nonlinear data fusion over Entity\u2013Relation graphs for Drug\u2013Target Interaction prediction"],"prefix":"10.1093","volume":"39","author":[{"given":"Eugenio","family":"Mazzone","sequence":"first","affiliation":[{"name":"Department of Medical Sciences, University of Torino , 10123 Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yves","family":"Moreau","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, ESAT-STADIUS, KU Leuven , 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1811-4762","authenticated-orcid":false,"given":"Piero","family":"Fariselli","sequence":"additional","affiliation":[{"name":"Department of Medical Sciences, University of Torino , 10123 Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1157-1899","authenticated-orcid":false,"given":"Daniele","family":"Raimondi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, ESAT-STADIUS, KU Leuven , 3001 Leuven, 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