DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberFCC/1/1976-08-01
Permanent link to this recordhttp://hdl.handle.net/10754/625903
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AbstractMotivation 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.
CitationKulmanov 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.
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) [FCC/1/1976-08-01]
PublisherOxford University Press (OUP)
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.