کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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505604 | 864523 | 2011 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA–CG–SVM method A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA–CG–SVM method](/preview/png/505604.png)
Breast cancer resistance protein (BCRP) is one of the key multi-drug resistance proteins, which significantly influences the therapeutic effects of many drugs, particularly anti-cancer drugs. Thus, distinguishing between substrates and non-substrates of BCRP is important not only for clinical use but also for drug discovery and development. In this study, a prediction model of the substrates and non-substrates of BCRP was developed using a modified support vector machine (SVM) method, namely GA–CG–SVM. The overall prediction accuracy of the established GA–CG–SVM model is 91.3% for the training set and 85.0% for an independent validation set. For comparison, two other machine learning methods, namely, C4.5 DT and k-NN, were also adopted to build prediction models. The results show that the GA–CG–SVM model is significantly superior to C4.5 DT and k-NN models in terms of the prediction accuracy. To sum up, the prediction model of BCRP substrates and non-substrates generated by the GA–CG–SVM method is sufficiently good and could be used as a screening tool for identifying the substrates and non-substrates of BCRP.
Journal: Computers in Biology and Medicine - Volume 41, Issue 11, November 2011, Pages 1006–1013