StochHMM: A flexible hidden Markov model tool and C++ library

Paul C. Lott; Ian F Korf

doi:10.1093/bioinformatics/btu057

StochHMM: A flexible hidden Markov model tool and C++ library

Paul C. Lott, Ian F Korf

Molecular and Cellular Biology

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Summary: Hidden Markov models (HMMs) are probabilistic models that are well-suited to solve many different classification problems in computation biology. StochHMM provides a command-line program and C++ library that can implement a traditional HMM from a simple text file. StochHMM provides researchers the flexibility to create higher-order emissions, integrate additional data sources and/or user-defined functions into multiple points within the HMM framework. Additional features include user-defined alphabets, ability to handle ambiguous characters in an emission-dependent manner, user-defined weighting of state paths and ability to tie transition probabilities to sequence.

Original language	English (US)
Pages (from-to)	1625-1626
Number of pages	2
Journal	Bioinformatics
Volume	30
Issue number	11
DOIs	https://doi.org/10.1093/bioinformatics/btu057
State	Published - Jun 1 2014

ASJC Scopus subject areas

Biochemistry
Molecular Biology
Computational Theory and Mathematics
Computer Science Applications
Computational Mathematics
Statistics and Probability
Medicine(all)

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StochHMM: A flexible hidden Markov model tool and C++ library

Abstract

ASJC Scopus subject areas

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