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Article: Detecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization

TitleDetecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization
Authors
KeywordsCommunity
Drug–drug interaction
Regularization
Semi-nonnegative matrix factorization
Weak balance theory
Issue Date2019
PublisherBioMed Central Ltd. The Journal's web site is located at https://jcheminf.biomedcentral.com/
Citation
Journal of Cheminformatics, 2019, v. 11, p. 28:1-28:16 How to Cite?
AbstractBACKGROUND: Because drug-drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work. RESULTS: This work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs. CONCLUSIONS: Owing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario.
Persistent Identifierhttp://hdl.handle.net/10722/277569
ISSN
2017 Impact Factor: 3.893
2015 SCImago Journal Rankings: 1.746
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShi, J-Y-
dc.contributor.authorMao, K-T-
dc.contributor.authorYu, H-
dc.contributor.authorYiu, S-M-
dc.date.accessioned2019-09-20T08:53:33Z-
dc.date.available2019-09-20T08:53:33Z-
dc.date.issued2019-
dc.identifier.citationJournal of Cheminformatics, 2019, v. 11, p. 28:1-28:16-
dc.identifier.issn1758-2946-
dc.identifier.urihttp://hdl.handle.net/10722/277569-
dc.description.abstractBACKGROUND: Because drug-drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work. RESULTS: This work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs. CONCLUSIONS: Owing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at https://jcheminf.biomedcentral.com/-
dc.relation.ispartofJournal of Cheminformatics-
dc.rightsJournal of Cheminformatics. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCommunity-
dc.subjectDrug–drug interaction-
dc.subjectRegularization-
dc.subjectSemi-nonnegative matrix factorization-
dc.subjectWeak balance theory-
dc.titleDetecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization-
dc.typeArticle-
dc.identifier.emailYiu, S-M: smyiu@cs.hku.hk-
dc.identifier.authorityYiu, S-M=rp00207-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13321-019-0352-9-
dc.identifier.pmid30963300-
dc.identifier.pmcidPMC6454721-
dc.identifier.hkuros305930-
dc.identifier.volume11-
dc.identifier.spage28:1-
dc.identifier.epage28:16-
dc.identifier.isiWOS:000464197000001-
dc.publisher.placeUnited Kingdom-

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