NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity
Author(s) -
Meet Barot,
Vladimir Gligorijević,
Kyunghyun Cho,
Richard Bonneau
Publication year - 2021
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab098
Subject(s) - computer science , annotation , protein function prediction , context (archaeology) , similarity (geometry) , artificial intelligence , artificial neural network , machine learning , function (biology) , proteome , network analysis , computational biology , organism , gene ontology , data mining , protein function , bioinformatics , biology , gene , genetics , paleontology , gene expression , physics , quantum mechanics , image (mathematics)
Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks.
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