Features Extraction For Protein Homology Detection Using Hidden Markov Models Combining Scores

Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discriminative approach, and in particular support vector machines for protein homologies detection. However, in place of the Fisher scoring method, we present a new Hidden Markov Model Combining Scores approach. Six scoring algorithms are combined as a way of extracting features from a protein sequence. Experiments show that our method, improves on previous methods for homologies detection of protein domains.