title: MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search.
abstract: We introduce MRFy, a tool for protein remote homology detection that captures beta-strand dependencies in the Markov random field. Over a set of 11 SCOP beta-structural superfamilies, MRFy shows a 14 percent improvement in mean Area Under the Curve for the motif recognition problem as compared to HMMER, 25 percent improvement as compared to RAPTOR, 14 percent improvement as compared to HHPred, and a 18 percent improvement as compared to CNFPred and RaptorX. MRFy was implemented in the Haskell functional programming language, and parallelizes well on multi-core systems. MRFy is available, as source code as well as an executable, from http://mrfy.cs.tufts.edu/.
authors.forename: Noah M
authors.initials: NM
authors.lastname: Daniels
authors.forename: Andrew
authors.initials: A
authors.lastname: Gallant
authors.forename: Norman
authors.initials: N
authors.lastname: Ramsey
authors.forename: Lenore J
authors.initials: LJ
authors.lastname: Cowen
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D013269:Stochastic Processes
chemical_list: D011506:Proteins