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RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

BACKGROUND: It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a consid...

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Autores principales: Fahimian, Ghazale, Zahiri, Javad, Arab, Seyed Shahriar, Sajedi, Reza H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532104/
https://www.ncbi.nlm.nih.gov/pubmed/33008415
http://dx.doi.org/10.1186/s12967-020-02541-3
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author Fahimian, Ghazale
Zahiri, Javad
Arab, Seyed Shahriar
Sajedi, Reza H.
author_facet Fahimian, Ghazale
Zahiri, Javad
Arab, Seyed Shahriar
Sajedi, Reza H.
author_sort Fahimian, Ghazale
collection PubMed
description BACKGROUND: It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. METHODS: In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. RESULTS: The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. CONCLUSION: Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.
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spelling pubmed-75321042020-10-05 RepCOOL: computational drug repositioning via integrating heterogeneous biological networks Fahimian, Ghazale Zahiri, Javad Arab, Seyed Shahriar Sajedi, Reza H. J Transl Med Research BACKGROUND: It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. METHODS: In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. RESULTS: The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. CONCLUSION: Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen. BioMed Central 2020-10-02 /pmc/articles/PMC7532104/ /pubmed/33008415 http://dx.doi.org/10.1186/s12967-020-02541-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fahimian, Ghazale
Zahiri, Javad
Arab, Seyed Shahriar
Sajedi, Reza H.
RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title_full RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title_fullStr RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title_full_unstemmed RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title_short RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
title_sort repcool: computational drug repositioning via integrating heterogeneous biological networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532104/
https://www.ncbi.nlm.nih.gov/pubmed/33008415
http://dx.doi.org/10.1186/s12967-020-02541-3
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