Show simple item record

dc.contributor.authorKulmanov, Maxat
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2019-08-08T13:17:31Z
dc.date.available2019-08-08T13:17:31Z
dc.date.issued2019-07-27
dc.identifier.citationKulmanov, M., & Hoehndorf, R. (2019). DeepGOPlus: Improved protein function prediction from sequence. Bioinformatics. doi:10.1093/bioinformatics/btz595
dc.identifier.doi10.1093/bioinformatics/btz595
dc.identifier.doi10.1101/615260
dc.identifier.urihttp://hdl.handle.net/10754/656444
dc.description.abstractMOTIVATION:Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein-protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time. RESULTS:We developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins (if available). We evaluate the performance of DeepGOPlus using the CAFA3 evaluation measures and achieve an Fmax of 0:390, 0:557 and 0:614 for BPO, MFO and CCO evaluations, respectively. These results would have made DeepGOPlus one of the three best predictors in CCO and the second best performing method in the BPO and MFO evaluations. We also compare DeepGOPlus with state-of-the-art methods such as DeepText2GO and GOLabeler on another dataset. DeepGOPlus can annotate around 40 protein sequences per second on common hardware, thereby making fast and accurate function predictions available for a wide range of proteins. AVAILABILITY:http://deepgoplus.bio2vec.net/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
dc.description.sponsorshipWe acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.
dc.description.sponsorshipFunding: This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976- 08-01, and FCS/1/3657-02-01.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz595/5539866
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.titleDeepGOPlus: Improved protein function prediction from sequence.
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 (Oxford, England)
dc.eprint.versionPublisher's Version/PDF
kaust.personKulmanov, Maxat
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3454-01-01
kaust.grant.numberURF/1/3790-01-01
kaust.grant.numberFCC/1/1976- 08-01
kaust.grant.numberFCS/1/3657-02-01
dc.relation.issupplementedbygithub:bio-ontology-research-group/deepgoplus
dc.relation.issupplementedbygithub:bio-ontology-research-group/deepgo
refterms.dateFOA2019-08-08T13:18:47Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: bio-ontology-research-group/deepgoplus: DeepGO with GOPlus axioms. Publication Date: 2018-12-31. github: <a href="https://github.com/bio-ontology-research-group/deepgoplus" >bio-ontology-research-group/deepgoplus</a> Handle: <a href="http://hdl.handle.net/10754/667024" >10754/667024</a></a></li><li><i>[Software]</i> <br/> Title: bio-ontology-research-group/deepgo: Function prediction using a deep ontology-aware classifier. Publication Date: 2016-04-28. github: <a href="https://github.com/bio-ontology-research-group/deepgo" >bio-ontology-research-group/deepgo</a> Handle: <a href="http://hdl.handle.net/10754/667034" >10754/667034</a></a></li></ul>
kaust.acknowledged.supportUnitSupercomputing Core Laboratory
dc.date.posted2019-04-23


Files in this item

Thumbnail
Name:
btz595.pdf
Size:
798.8Kb
Format:
PDF
Description:
Published version
Thumbnail
Name:
btz595_supplementary_data.pdf
Size:
49.27Kb
Format:
PDF
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.