title: Parallel cascade identification as a means for automatically classifying protein sequences into structure/function groups.
abstract: Current methods for automatically classifying protein sequences into structure/function groups, based on their hydrophobicity profiles, have typically required large training sets. The most successful of these methods are based on hidden Markov models, but may require hundreds of exemplars for training in order to obtain consistent results. In this paper, we describe a new approach, based on nonlinear system identification, which appears to require little training data to achieve highly promising results.
authors.affiliation: Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada. korenber@post.queensu.ca
authors.forename: M
authors.initials: M
authors.lastname: Korenberg
authors.forename: J E
authors.initials: JE
authors.lastname: Solomon
authors.forename: M E
authors.initials: ME
authors.lastname: Regelson
mesh_terms: D000818:Animals; D006801:Humans; D011506:Proteins / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013269:Stochastic Processes
chemical_list: D011506:Proteins