Show simple item record

dc.contributor.authorKulmanov, Maxat
dc.contributor.authorKhan, Ameer Mohammed Asif
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2017-10-19T07:10:41Z
dc.date.available2017-10-19T07:10:41Z
dc.date.issued2017-09-27
dc.identifier.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.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/btx624
dc.identifier.urihttp://hdl.handle.net/10754/625903
dc.description.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.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) [FCC/1/1976-08-01]
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx624/4265461/DeepGO-predicting-protein-functions-from-sequence
dc.rightsThis 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.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.identifier.arxivid1705.05919
kaust.personKulmanov, Maxat
kaust.personKhan, Ameer Mohammed Asif
kaust.personHoehndorf, Robert
kaust.grant.numberFCC/1/1976-08-01
dc.versionv1
refterms.dateFOA2018-06-14T02:49:56Z


Files in this item

Thumbnail
Name:
btx624.pdf
Size:
497.5Kb
Format:
PDF
Description:
Main article
Thumbnail
Name:
btx624_Supp.pdf
Size:
71.33Kb
Format:
PDF
Description:
Supplemental files
Thumbnail
Name:
btx624_Supp_train_cluster.zip
Size:
387.1Kb
Format:
Unknown
Description:
Supplemental files

This item appears in the following Collection(s)

Show simple item record

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.
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.