A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites.

Long, Haixia; Liao, Bo; Xu, Xingyu; Yang, Jialiang

Long, Haixia; Liao, Bo; Xu, Xingyu; Yang, Jialiang.

Affiliation

Long H; Department of Information Science and Technology, Hainan Normal University, Haikou 571158, China. myresearch_hainnu@163.com.
Liao B; Department of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China. dragonbw@163.com.
Xu X; College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China. xingyuxu821@163.com.
Yang J; Department of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China. jialiang.yang@mssm.edu.

Int J Mol Sci ; 19(9)2018 Sep 18.

Article in En | MEDLINE | ID: mdl-30231550

Subject(s)

Deep Learning; Hydroxylysine/chemistry; Hydroxyproline/chemistry; Proteins/chemistry; Humans; Hydroxylation; Hydroxylysine/metabolism; Hydroxyproline/metabolism; Models, Biological; Neural Networks, Computer; Protein Processing, Post-Translational; Proteins/metabolism

Key words

convolutional neural network (CNN); hydroxylation sites; iHyd-PseAAC; iHyd-PseCp; long short-term memory network (LSTM); protein post-translational modification (PTM)

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Deep Learning / Hydroxylysine / Hydroxyproline Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Int J Mol Sci Year: 2018 Document type: Article Affiliation country: China Country of publication: Switzerland

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