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

@article{Zaki2004FeaturesEF,
  title={Features Extraction For Protein Homology Detection Using Hidden Markov Models Combining Scores},
  author={Nazar Zaki and Safaai Deris and Rosli Md. Illias},
  journal={International Journal of Computational Intelligence and Applications},
  year={2004},
  volume={4},
  pages={1-12}
}
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… CONTINUE READING
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