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DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

Authors :
Maxat Kulmanov
Mohammed Asif Khan
Robert Hoehndorf
Source :
Bioinformatics
Publication Year :
2017
Publisher :
Oxford University Press (OUP), 2017.

Abstract

Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Availability and implementation Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo Supplementary information Supplementary data are available at Bioinformatics online.

Details

ISSN :
13674811 and 13674803
Volume :
34
Database :
OpenAIRE
Journal :
Bioinformatics
Accession number :
edsair.doi.dedup.....8a8610fbe7bdedbcfd206c289f4b36b6
Full Text :
https://doi.org/10.1093/bioinformatics/btx624