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- W2366884065 abstract "Objective: Combined with the SVM technology to study a new and effective method of drug target prediction based on the protein primary structure similarity of known drug targets and potential drug targets.Methods: To construct the training set,extract the primary structure characteristics of protein sequences,and preprocess the data,then select the optimal kernel function and parameters,finally do feature selection,and train the best forecasting model,test its effect.Apply the optimal prediction model on the prediction set that composed of GPCRS for potential drug targets mining.Results: The average prediction accuracy of the optimal classification model that based on SVM is 81.03%.Applying the optimal classification model on the prediction set of G-proteins,then we find that some of the proteins that ranking top 20 of the prediction results are related with some certain diseases.Particularly,there are two G-proteins seem as clinical trials drug targets in the therapeutic target database(TTD).Conclusions: This drug target prediction method based on SVM and protein sequence features is effective,and the application of this method to predict potential drug targets can provide a valuable reference for the discovery of new drug targets." @default.
- W2366884065 created "2016-06-24" @default.
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- W2366884065 date "2012-01-01" @default.
- W2366884065 modified "2023-09-23" @default.
- W2366884065 title "A Method of Drug Target Prediction Based on SVM and its Application" @default.
- W2366884065 hasPublicationYear "2012" @default.
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