title: Using the Fisher kernel method to detect remote protein homologies.
abstract: 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.
authors.affiliation: Department of Computer Science, University of California, Santa Cruz 95064, USA. tommi@ai.mit.edu
authors.forename: T
authors.initials: T
authors.lastname: Jaakkola
authors.forename: M
authors.initials: M
authors.lastname: Diekhans
authors.forename: D
authors.initials: D
authors.lastname: Haussler
mesh_terms: D016208:Databases, Factual; D019204:GTP-Binding Proteins / Q000737:chemistry; D008390:Markov Chains; D015233:Models, Statistical; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins; D019204:GTP-Binding Proteins