DeepPhos: prediction of protein phosphorylation sites with deep learning.

ABSTRACT:

Motivation

Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. 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.

Results

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 m

SUBMITTER: Luo F 

PROVIDER: S-EPMC6691328 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature