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DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artifi...

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Detalles Bibliográficos
Autores principales: Islam, Sk Mazharul, Hossain, Sk Md Mosaddek, Ray, Sumanta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894953/
https://www.ncbi.nlm.nih.gov/pubmed/33606741
http://dx.doi.org/10.1371/journal.pone.0246920
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author Islam, Sk Mazharul
Hossain, Sk Md Mosaddek
Ray, Sumanta
author_facet Islam, Sk Mazharul
Hossain, Sk Md Mosaddek
Ray, Sumanta
author_sort Islam, Sk Mazharul
collection PubMed
description In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).
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spelling pubmed-78949532021-03-01 DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation Islam, Sk Mazharul Hossain, Sk Md Mosaddek Ray, Sumanta PLoS One Research Article In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)). Public Library of Science 2021-02-19 /pmc/articles/PMC7894953/ /pubmed/33606741 http://dx.doi.org/10.1371/journal.pone.0246920 Text en © 2021 Islam et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Islam, Sk Mazharul
Hossain, Sk Md Mosaddek
Ray, Sumanta
DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title_full DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title_fullStr DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title_full_unstemmed DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title_short DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
title_sort dti-snnfra: drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894953/
https://www.ncbi.nlm.nih.gov/pubmed/33606741
http://dx.doi.org/10.1371/journal.pone.0246920
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