{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:54:45Z","timestamp":1775915685425,"version":"3.50.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Edmond J. Safra Center for Bioinformatics"},{"DOI":"10.13039\/501100004375","name":"Tel-Aviv University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004375","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100027426","name":"Schmidt Futures","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100027426","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1OT2OD032742"],"award-info":[{"award-number":["1OT2OD032742"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["2417\/20"],"award-info":[{"award-number":["2417\/20"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein\u2013protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>https:\/\/github.com\/raminass\/protein-contrastive<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>raminass@gmail.com<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae387","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T07:38:39Z","timestamp":1725521919000},"page":"ii105-ii110","source":"Crossref","is-referenced-by-count":3,"title":["Multi-modal contrastive learning of subcellular organization using DICE"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2670-6856","authenticated-orcid":false,"given":"Rami","family":"Nasser","sequence":"first","affiliation":[{"name":"School of Computer Science, Tel Aviv University , Tel Aviv 69978,","place":["Israel"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leah V","family":"Schaffer","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of California, San Diego , San Diego, CA 92037,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trey","family":"Ideker","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of California, San Diego , San Diego, CA 92037,","place":["United States"]},{"name":"Department of Computer Science and Engineering, University of California, San Diego , San Diego, CA 92037,","place":["United States"]},{"name":"Moores Cancer Center, University of California, San Diego , San Diego, CA 92037,","place":["United States"]},{"name":"Department of Bioengineering, University of California, San Diego , San Diego, CA 92037,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roded","family":"Sharan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Tel Aviv University , Tel Aviv 69978,","place":["Israel"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2024102913475350500_btae387-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 Genet"},{"key":"2024102913475350500_btae387-B2","article-title":"Learning representations by maximizing mutual information across views","volume":"32","author":"Bachman","year":"2019","journal-title":"Adv Neural Inf 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