Biomedical subjects
Publications and source records attributed to M Diekhans.
This paper presents results of blind predictions submitted to the CASP3 protein structure prediction experiment. We made predictions using the SAM-T98 method, an iterative hidden Markov model-based method for constructing protein family profiles. The method is purely sequence-based, using no structural information, and yet was able to predict structures as well as all but five of the structure-based methods in CASP3.
A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hidden Markov model. The general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.
A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a generative statistical model for a protein family, in this case a hidden Markov model. This general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.