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BalestraWeb: efficient online evaluation of drug–target interactions

Summary: BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug–target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify...

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Detalles Bibliográficos
Autores principales: Cobanoglu, Murat Can, Oltvai, Zoltán N., Taylor, D. Lansing, Bahar, Ivet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271144/
https://www.ncbi.nlm.nih.gov/pubmed/25192741
http://dx.doi.org/10.1093/bioinformatics/btu599
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author Cobanoglu, Murat Can
Oltvai, Zoltán N.
Taylor, D. Lansing
Bahar, Ivet
author_facet Cobanoglu, Murat Can
Oltvai, Zoltán N.
Taylor, D. Lansing
Bahar, Ivet
author_sort Cobanoglu, Murat Can
collection PubMed
description Summary: BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug–target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects. Availability and implementation: BalestraWeb is accessible at http://balestra.csb.pitt.edu/. The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit. The server is implemented using Python, Flask, NumPy and SciPy. Contact: bahar@pitt.edu
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spelling pubmed-42711442015-01-08 BalestraWeb: efficient online evaluation of drug–target interactions Cobanoglu, Murat Can Oltvai, Zoltán N. Taylor, D. Lansing Bahar, Ivet Bioinformatics Applications Notes Summary: BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug–target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects. Availability and implementation: BalestraWeb is accessible at http://balestra.csb.pitt.edu/. The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit. The server is implemented using Python, Flask, NumPy and SciPy. Contact: bahar@pitt.edu Oxford University Press 2015-01-01 2014-09-05 /pmc/articles/PMC4271144/ /pubmed/25192741 http://dx.doi.org/10.1093/bioinformatics/btu599 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Cobanoglu, Murat Can
Oltvai, Zoltán N.
Taylor, D. Lansing
Bahar, Ivet
BalestraWeb: efficient online evaluation of drug–target interactions
title BalestraWeb: efficient online evaluation of drug–target interactions
title_full BalestraWeb: efficient online evaluation of drug–target interactions
title_fullStr BalestraWeb: efficient online evaluation of drug–target interactions
title_full_unstemmed BalestraWeb: efficient online evaluation of drug–target interactions
title_short BalestraWeb: efficient online evaluation of drug–target interactions
title_sort balestraweb: efficient online evaluation of drug–target interactions
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271144/
https://www.ncbi.nlm.nih.gov/pubmed/25192741
http://dx.doi.org/10.1093/bioinformatics/btu599
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