Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification

Abstract 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 learn...

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Autores principales:	Lingwei Xie, Song He, Yuqi Wen, Xiaochen Bo, Zhongnan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b013c36805df4c8d9dc018e1d5349f5c
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spelling	oai:doaj.org-article:b013c36805df4c8d9dc018e1d5349f5c2021-12-02T16:08:23ZDiscovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification10.1038/s41598-017-07705-82045-2322https://doaj.org/article/b013c36805df4c8d9dc018e1d5349f5c2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07705-8https://doaj.org/toc/2045-2322Abstract 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.Lingwei XieSong HeYuqi WenXiaochen BoZhongnan ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lingwei Xie Song He Yuqi Wen Xiaochen Bo Zhongnan Zhang Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification
description	Abstract 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	article
author	Lingwei Xie Song He Yuqi Wen Xiaochen Bo Zhongnan Zhang
author_facet	Lingwei Xie Song He Yuqi Wen Xiaochen Bo Zhongnan Zhang
author_sort	Lingwei Xie
title	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_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_sort	discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b013c36805df4c8d9dc018e1d5349f5c
work_keys_str_mv	AT lingweixie discoveryofnoveltherapeuticpropertiesofdrugsfromtranscriptionalresponsesbasedonmultilabelclassification AT songhe discoveryofnoveltherapeuticpropertiesofdrugsfromtranscriptionalresponsesbasedonmultilabelclassification AT yuqiwen discoveryofnoveltherapeuticpropertiesofdrugsfromtranscriptionalresponsesbasedonmultilabelclassification AT xiaochenbo discoveryofnoveltherapeuticpropertiesofdrugsfromtranscriptionalresponsesbasedonmultilabelclassification AT zhongnanzhang discoveryofnoveltherapeuticpropertiesofdrugsfromtranscriptionalresponsesbasedonmultilabelclassification
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Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification

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