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Issue No. 01 - Jan.-Feb. (2015 vol. 12)
ISSN: 1545-5963
pp: 4-16
Noah M. Daniels , Mathematics Department and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA
Andrew Gallant , Department of Computer Science, Tufts University, Medford, MA
Norman Ramsey , Department of Computer Science, Tufts University, Medford, MA
Lenore J. Cowen , Department of Computer Science, Tufts University, Medford, MA
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/.
INDEX TERMS
Markov processes, Hidden Markov models, Computational modeling, Viterbi algorithm, Simulated annealing, Search problems,structural bioinformatics, Protein structure prediction, remote homology detection
CITATION
Noah M. Daniels, Andrew Gallant, Norman Ramsey, Lenore J. Cowen, "MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 12, no. , pp. 4-16, Jan.-Feb. 2015, doi:10.1109/TCBB.2014.2344682
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