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Inferring Protein Domain Semantic Roles Using word2vec

Daniel Buchan, David Jones
doi: https://doi.org/10.1101/617647
Daniel Buchan
University College London
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  • For correspondence: daniel.buchan@ucl.ac.uk
David Jones
University College London
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Abstract

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 24, 2019.
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Inferring Protein Domain Semantic Roles Using word2vec
Daniel Buchan, David Jones
bioRxiv 617647; doi: https://doi.org/10.1101/617647
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Inferring Protein Domain Semantic Roles Using word2vec
Daniel Buchan, David Jones
bioRxiv 617647; doi: https://doi.org/10.1101/617647

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