A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction

摘要

The problem of secondary structure prediction can be formulated as a pattern classification problem and methods from statistics and machine learning are suitable. This paper proposes a new combination approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) by typical sample extraction based on a UBM/GMM system for SVM in protein secondary structure prediction. Our hybrid model achieved a good performance of three-state overall per residue accuracy Q_3 = 77.6% which is comparable to the best techniques available.