{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:50Z","timestamp":1772138090661,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"name":"Edmond J. Safra Center for Bioinformatics at Tel-Aviv University"},{"name":"Blavatnik Research Fund"},{"name":"Blavatnik Interdisciplinary Cyber Research Center in Tel-Aviv University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer protein-RNA binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed DLPRB (Deep Learning for Protein-RNA Binding), a new deep neural network (DNN) approach for learning intrinsic protein-RNA binding preferences and predicting novel interactions. We present two different network architectures: a convolutional neural network (CNN), and a recurrent neural network (RNN). The novelty of our network hinges upon two key aspects: (i) the joint analysis of both RNA sequence and structure, which is represented as a probability vector of different RNA structural contexts; (ii) novel features in the architecture of the networks, such as the application of RNNs to RNA-binding prediction, and the combination of hundreds of variable-length filters in the CNN. Our results in inferring accurate RNA-binding models from high-throughput in vitro data exhibit substantial improvements, compared to all previous approaches for protein-RNA binding prediction (both DNN and non-DNN based). A more modest, yet statistically significant, improvement is achieved for in vivo binding prediction. When incorporating experimentally-measured RNA structure, compared to predicted one, the improvement on in vivo data increases. By visualizing the binding specificities, we can gain biological insights underlying the mechanism of protein RNA-binding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is publicly available at https:\/\/github.com\/ilanbb\/dlprb.<\/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\/bty600","type":"journal-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T21:07:02Z","timestamp":1530824822000},"page":"i638-i646","source":"Crossref","is-referenced-by-count":82,"title":["A deep neural network approach for learning intrinsic protein-RNA binding preferences"],"prefix":"10.1093","volume":"34","author":[{"given":"Ilan","family":"Ben-Bassat","sequence":"first","affiliation":[{"name":"Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel"}]},{"given":"Benny","family":"Chor","sequence":"additional","affiliation":[{"name":"Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel"}]},{"given":"Yaron","family":"Orenstein","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061313491971100_bty600-B1","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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