Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction.

ABSTRACT:

Background

Recurrent neural network(RNN) is a good way to process sequential data, but the capability of RNN to compute long sequence data is inefficient. As a variant of RNN, long short term memory(LSTM) solved the problem in some extent. Here we improved LSTM for big data application in protein-protein interaction interface residue pairs prediction based on the following two reasons. On the one hand, there are some deficiencies in LSTM, such as shallow layers, gradient explosion or vanishing, etc. With a dramatic data increasing, the imbalance between algorithm innovation and big data processing has been more serious and urgent. On the other hand, protein-protein interaction interface residue pairs prediction is an important problem in biology, but the low prediction accuracy com

SUBMITTER: Liu J 

PROVIDER: S-EPMC6882172 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature