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Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein\u2013RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The EDCNN algorithm is available at GitHub: https:\/\/github.com\/yaweiwang1232\/EDCNN. Both the software and the supporting data can be downloaded from: https:\/\/figshare.com\/articles\/software\/EDCNN\/16803217.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab739","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T18:34:48Z","timestamp":1634754888000},"page":"678-686","source":"Crossref","is-referenced-by-count":11,"title":["EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network"],"prefix":"10.1093","volume":"38","author":[{"given":"Yawei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Changchun, Jilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-139X","authenticated-orcid":false,"given":"Yuning","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University , Changchun, Jilin, China"}]},{"given":"Zhiqiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University , Changchun, Jilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-733X","authenticated-orcid":false,"given":"Ka-Chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Kowloon Tong, Hong Kong SAR"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8716-9823","authenticated-orcid":false,"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Changchun, Jilin, China"}]}],"member":"286","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"2023020108473052100_btab739-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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