An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.

ABSTRACT: As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional mod

SUBMITTER: Hu S 

PROVIDER: S-EPMC6855455 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature