Predicting subcellular location of protein with evolution information and sequence-based deep learning.

ABSTRACT:

Background

Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations.

Results

Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as

SUBMITTER: Liao Z 

PROVIDER: S-EPMC8539821 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature