DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

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Type
Article
Authors
Kulmanov, Maxat cc
Zhapa-Camacho, Fernando cc
Hoehndorf, Robert cc
KAUST Department
Bio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant Number
FCC/1/1976-08-01
URF/1/3790-01-01
URF/1/4355-01-01
Date
2021-05-21
Online Publication Date
2021-05-21
Print Publication Date
2021-07-02
Submitted Date
2021-02-22
Permanent link to this record
http://hdl.handle.net/10754/669238

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Abstract
Abstract Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic Web technologies. DeepGOWeb provides accurate and fast predictions and ensures that predicted functions are consistent with the Gene Ontology; it can provide predictions for any protein and any function in Gene Ontology. DeepGOWeb is freely available at https://deepgo.cbrc.kaust.edu.sa/.
Citation
Kulmanov, M., Zhapa-Camacho, F., & Hoehndorf, R. (2021). DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web. Nucleic Acids Research. doi:10.1093/nar/gkab373
Sponsors
King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3790-01-01, URF/1/4355-01-01, FCC/1/1976-08-01, FCC/1/1976-08-08]. Funding for open access charge: King Abdullah University of Science and Technology.
Publisher
Oxford University Press (OUP)
Journal
Nucleic Acids Research
DOI
10.1093/nar/gkab373
Additional Links
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab373/6279831
Collections
Articles; Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, 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, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.