DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web
KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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
Permanent link to this recordhttp://hdl.handle.net/10754/669238
MetadataShow full item record
AbstractAbstract 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/.
CitationKulmanov, 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
SponsorsKing 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.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
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.