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Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of DeepPhos is publicly deposited at https:\/\/github.com\/USTCHIlab\/DeepPhos.<\/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\/bty1051","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T12:11:20Z","timestamp":1545999080000},"page":"2766-2773","source":"Crossref","is-referenced-by-count":175,"title":["DeepPhos: prediction of protein phosphorylation sites with deep learning"],"prefix":"10.1093","volume":"35","author":[{"given":"Fenglin","family":"Luo","sequence":"first","affiliation":[{"name":"School of Information Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology"},{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"2023062708551315100_bty1051-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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