Buchan, DW;
Jones, DT;
(2020)
Learning a Functional Grammar of Protein Domains using Natural Language Word Embedding Techniques.
Proteins
, 88
(4)
pp. 616-624.
10.1002/prot.25842.
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Abstract
In this paper, using word2vec, a widely-used natural language processing method, we demonstrate that proteins domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to multi-domain proteins in which they are found. Word2vec is a group of models which can be used to produce semantically meaningful embeddings of words or tokens in a fixed-dimension 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 [1] pfam domain assignments we observe that the embedding could be used to suggest putative GO assignments for Pfam [2] Domains of Unknown Function. This article is protected by copyright. All rights reserved.
| Type: | Article |
|---|---|
| Title: | Learning a Functional Grammar of Protein Domains using Natural Language Word Embedding Techniques |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/prot.25842 |
| Publisher version: | https://doi.org/10.1002/prot.25842 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Semantic embedding, function prediction, machine learning, protein domains, word2vec |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10086769 |
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