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Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification
Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algor...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5541064/ https://www.ncbi.nlm.nih.gov/pubmed/28769090 http://dx.doi.org/10.1038/s41598-017-07705-8 |
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author | Xie, Lingwei He, Song Wen, Yuqi Bo, Xiaochen Zhang, Zhongnan |
author_facet | Xie, Lingwei He, Song Wen, Yuqi Bo, Xiaochen Zhang, Zhongnan |
author_sort | Xie, Lingwei |
collection | PubMed |
description | Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algorithms have been applied successfully in biomedical research. In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model to predict previously unrecognized therapeutic properties of drugs based on LINCS transcriptome data. This approach to complete the said task has not been achieved in previous studies. By performing in silico comparison, we demonstrated that the proposed Softmax method showed markedly superior performance over those of other methods. Once fully trained, the method showed a training accuracy exceeding 80% and a validation accuracy of approximately 70%. We generated a highly credible set of 98 drugs with high potential to be repositioned for novel therapeutic purposes. Our case studies included zonisamide and brinzolamide, which were originally developed to treat indications of the nervous system and sensory organs, respectively. Both drugs were repurposed to the cardiovascular category. |
format | Online Article Text |
id | pubmed-5541064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55410642017-08-07 Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification Xie, Lingwei He, Song Wen, Yuqi Bo, Xiaochen Zhang, Zhongnan Sci Rep Article Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algorithms have been applied successfully in biomedical research. In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model to predict previously unrecognized therapeutic properties of drugs based on LINCS transcriptome data. This approach to complete the said task has not been achieved in previous studies. By performing in silico comparison, we demonstrated that the proposed Softmax method showed markedly superior performance over those of other methods. Once fully trained, the method showed a training accuracy exceeding 80% and a validation accuracy of approximately 70%. We generated a highly credible set of 98 drugs with high potential to be repositioned for novel therapeutic purposes. Our case studies included zonisamide and brinzolamide, which were originally developed to treat indications of the nervous system and sensory organs, respectively. Both drugs were repurposed to the cardiovascular category. Nature Publishing Group UK 2017-08-02 /pmc/articles/PMC5541064/ /pubmed/28769090 http://dx.doi.org/10.1038/s41598-017-07705-8 Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xie, Lingwei He, Song Wen, Yuqi Bo, Xiaochen Zhang, Zhongnan Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title | Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title_full | Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title_fullStr | Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title_full_unstemmed | Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title_short | Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
title_sort | discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5541064/ https://www.ncbi.nlm.nih.gov/pubmed/28769090 http://dx.doi.org/10.1038/s41598-017-07705-8 |
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