title: Representing and reasoning about protein families using generative and discriminative methods.
abstract: This work addresses the issues of data representation and incorporation of domain knowledge into the design of learning systems for reasoning about protein families. Given the limited expressive capacity of a particular method, a mixture of protein annotation and fold recognition experts, each implementing a different underlying representation, should provide a robust method for assigning sequences to families. These ideas are illustrated using two data-driven learning methods that make use of different prior information and employ independent, yet complementary, projections of a family: hidden Markov models (HMMs) based on a multiple sequence alignment and neural networks (NNs) based on global sequence descriptors of proteins. Examination of seven protein families indicates that combining a generative (HMM) and a discriminative (NN) method is better than either method on its own. Biologically, human 4-hydroxyphenylpyruvic acid dioxygenase, involved in tyrosinemia type 3, is predicted to be structurally and functionally related to the glyoxalase I family.
authors.affiliation: Department of Molecular and Cell Biology (MS 74-197), Radiation Biology and Environmental Toxicology Group, Life Sciences Division, Lawrence Berkeley National Laboratory, Cyclotron Road, Berkeley, CA 94720, USA.
authors.forename: I S
authors.initials: IS
authors.lastname: Mian
authors.forename: I
authors.initials: I
authors.lastname: Dubchak
mesh_terms: D000595:Amino Acid Sequence; D008390:Markov Chains; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods
chemical_list: D011506:Proteins