Prediction of cancer drugs by chemical-chemical interactions.

Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the ord...

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Autores principales:	Jing Lu, Guohua Huang, Hai-Peng Li, Kai-Yan Feng, Lei Chen, Ming-Yue Zheng, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ce38ee7da274a9e9c2b301e545f283e
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id	oai:doaj.org-article:0ce38ee7da274a9e9c2b301e545f283e
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spelling	oai:doaj.org-article:0ce38ee7da274a9e9c2b301e545f283e2021-11-18T08:34:11ZPrediction of cancer drugs by chemical-chemical interactions.1932-620310.1371/journal.pone.0087791https://doaj.org/article/0ce38ee7da274a9e9c2b301e545f283e2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24498372/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.Jing LuGuohua HuangHai-Peng LiKai-Yan FengLei ChenMing-Yue ZhengYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e87791 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jing Lu Guohua Huang Hai-Peng Li Kai-Yan Feng Lei Chen Ming-Yue Zheng Yu-Dong Cai Prediction of cancer drugs by chemical-chemical interactions.
description	Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.
format	article
author	Jing Lu Guohua Huang Hai-Peng Li Kai-Yan Feng Lei Chen Ming-Yue Zheng Yu-Dong Cai
author_facet	Jing Lu Guohua Huang Hai-Peng Li Kai-Yan Feng Lei Chen Ming-Yue Zheng Yu-Dong Cai
author_sort	Jing Lu
title	Prediction of cancer drugs by chemical-chemical interactions.
title_short	Prediction of cancer drugs by chemical-chemical interactions.
title_full	Prediction of cancer drugs by chemical-chemical interactions.
title_fullStr	Prediction of cancer drugs by chemical-chemical interactions.
title_full_unstemmed	Prediction of cancer drugs by chemical-chemical interactions.
title_sort	prediction of cancer drugs by chemical-chemical interactions.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0ce38ee7da274a9e9c2b301e545f283e
work_keys_str_mv	AT jinglu predictionofcancerdrugsbychemicalchemicalinteractions AT guohuahuang predictionofcancerdrugsbychemicalchemicalinteractions AT haipengli predictionofcancerdrugsbychemicalchemicalinteractions AT kaiyanfeng predictionofcancerdrugsbychemicalchemicalinteractions AT leichen predictionofcancerdrugsbychemicalchemicalinteractions AT mingyuezheng predictionofcancerdrugsbychemicalchemicalinteractions AT yudongcai predictionofcancerdrugsbychemicalchemicalinteractions
_version_	1718421603132375040

Prediction of cancer drugs by chemical-chemical interactions.

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