RT Journal Article
SR Electronic
T1 Inferring Protein Domain Semantic Roles Using word2vec
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 617647
DO 10.1101/617647
A1 Buchan, Daniel
A1 Jones, David
YR 2019
UL http://biorxiv.org/content/early/2019/04/24/617647.abstract
AB In this paper, using word2vec, we demonstrate that proteins domains may have semantic “meaning” in the context of multi-domain proteins. Word2vec is a group of models which can be used to produce semantically meaningful embeddings of words or tokens in a vector space. In this work we treat multi-domain proteins as “sentences” where domain identifiers are tokens which may be considered as “words”. Using all Interpro (Finn, Attwood et al. 2017) eukaryotic proteins as a corpus of “sentences” we demonstrate that Word2vec creates functionally meaningful embeddings of protein domains. We additionally show how this can be applied to identifying the putative functional roles for Pfam (Finn, Coggill et al. 2016) Domains of Unknown Function.