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• W2022045489 abstract "Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC." @default.
• W2022045489 created "2016-06-24" @default.
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• W2022045489 date "2014-01-01" @default.
• W2022045489 modified "2023-10-16" @default.
• W2022045489 title "Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities" @default.
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• W2022045489 doi "https://doi.org/10.1155/2014/464093" @default.
• W2022045489 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4122092" @default.
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