Abstract
Hot spot prediction in protein interfaces is very important for understanding the essence of protein interactions and may provide promising prospect for drug design. Since experimental approaches such as alanine scanning mutagenesis are cost-expensive and time-consuming, reliable computational methods are needed. In this paper, a systematic method based on least squares support vector machine (LS-SVM) within the Bayesian evidence framework is proposed, where three levels Bayesian inferences are used to determine the model parameters and regularization hyper-parameters. Then a higher precision model for hot spots is constructed by optimizing these parameters. Compared with the previous methods, our model appears to be better performance.
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Qi, J., Zhang, X., Li, B. (2013). Protein Interaction Hot Spots Prediction Using LS-SVM within the Bayesian Interpretation. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_18
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DOI: https://doi.org/10.1007/978-3-642-53917-6_18
Publisher Name: Springer, Berlin, Heidelberg
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