DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
Type
ArticleKAUST Department
Bio-Ontology Research Group (BORG)Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
FCC/1/1976-08-01Date
2017-09-27Permanent link to this record
http://hdl.handle.net/10754/625903
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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.Citation
Kulmanov M, Khan MA, Hoehndorf R (2017) DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx624.Sponsors
This work was supported by funding from King Abdullah University of Science and Technology (KAUST) [FCC/1/1976-08-01]Publisher
Oxford University Press (OUP)Journal
BioinformaticsarXiv
1705.05919Additional Links
https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx624/4265461/DeepGO-predicting-protein-functions-from-sequenceae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btx624
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