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
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Acceso en línea:https://doaj.org/article/0ce38ee7da274a9e9c2b301e545f283e
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Sumario: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.