Comput. Biol. Med. - A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method.

Tópicos

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Resumo

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.

Resumo Limpo

breast cancer resist protein bcrp one key multidrug resist protein signific influenc therapeut effect mani drug particular anticanc drug thus distinguish substrat nonsubstr bcrp import clinic use also drug discoveri develop studi predict model substrat nonsubstr bcrp develop use modifi support vector machin svm method name gacgsvm overal predict accuraci establish gacgsvm model train set independ valid set comparison two machin learn method name c dt knn also adopt build predict model result show gacgsvm model signific superior c dt knn model term predict accuraci sum predict model bcrp substrat nonsubstr generat gacgsvm method suffici good use screen tool identifi substrat nonsubstr bcrp

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