Molecular cavity topological representation for pattern analysis: A NLP analogy-based word2vec method

Guo, D; Wang, Q; Liang, M; Liu, W; Nie, J

Molecular cavity topological representation for pattern analysis: A NLP analogy-based word2vec method

Guo, D Wang, Q Liang, M Liu, W

Nie, J

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Publication Type:: Journal Article
Citation:: International Journal of Molecular Sciences, 2019, 20 (23)
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Guo, D	en_US
dc.contributor.author	Wang, Q	en_US
dc.contributor.author	Liang, M	en_US
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313	en_US
dc.contributor.author	Nie, J	en_US
dc.date.available	2019-11-26	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	International Journal of Molecular Sciences, 2019, 20 (23)	en_US
dc.identifier.issn	1661-6596	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137555
dc.description.abstract	© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension reduction and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities.	en_US
dc.relation.ispartof	International Journal of Molecular Sciences	en_US
dc.relation.isbasedon	10.3390/ijms20236019	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Chemical Physics	en_US
dc.title	Molecular cavity topological representation for pattern analysis: A NLP analogy-based word2vec method	en_US
dc.type	Journal Article
utslib.citation.volume	23	en_US
utslib.citation.volume	20	en_US
utslib.for	0399 Other Chemical Sciences	en_US
utslib.for	0604 Genetics	en_US
utslib.for	0699 Other Biological Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access	*
pubs.issue	23	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension reduction and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities.

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http://hdl.handle.net/10453/137555