Type
ArticleAuthors
Kulmanov, Maxat
Hoehndorf, Robert

KAUST 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
URF/1/3454-01-01URF/1/3790-01-01
FCC/1/1976- 08-01
FCS/1/3657-02-01
Date
2019-07-27Preprint Posting Date
2019-04-23Permanent link to this record
http://hdl.handle.net/10754/656444
Metadata
Show full item recordAbstract
MOTIVATION: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.Citation
Kulmanov, M., & Hoehndorf, R. (2019). DeepGOPlus: Improved protein function prediction from sequence. Bioinformatics. doi:10.1093/bioinformatics/btz595Sponsors
We acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.Funding: 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.
Publisher
Oxford University Press (OUP)Journal
Bioinformatics (Oxford, England)Additional Links
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz595/5539866Relations
Is Supplemented By:- [Software]
Title: bio-ontology-research-group/deepgoplus: DeepGO with GOPlus axioms. Publication Date: 2018-12-31. github: bio-ontology-research-group/deepgoplus Handle: 10754/667024 - [Software]
Title: bio-ontology-research-group/deepgo: Function prediction using a deep ontology-aware classifier. Publication Date: 2016-04-28. github: bio-ontology-research-group/deepgo Handle: 10754/667034
ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btz595
Scopus Count
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.