Protein remote homology detection based on bidirectional long short-term memory

By Shumin Li, Junjie Chen, Bin Liu. 2017

62 citations recorded by the source corpus.

Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection. In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer. Experimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.

Subfields: Biophysics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Wojciech Samek
2. Leyi Wei
3. K. Müller
4. Grgoire Montavon

In Molecular Biology

1. Markus A. Wenzel
2. Quan Zou
3. Nils Strodthoff
4. Wojciech Samek
5. Leyi Wei
6. Patrick Wagner

Citations in the corpus

• UDSMProt: universal deep sequence models for protein classification (2019, methodology): “We will compare to ProDec-BLSTM [6] with a bidirectional recurrent neural network operating on PSSM input features building on earlier work [31].”
• Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA (2018, background): “Recently, many studies on biological computing prediction classifications (Liu and Li 2018; Li et al. 2017a; Liu et al. 2017c; Zeng et al. 2018) involving CNNs have used convolution kernels of the first layer to extract informative motifs from massive sequence data sets.”