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

Paul C. Lott, Ian F Korf

Research output: Contribution to journalArticle

8 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 languageEnglish (US)
Pages (from-to)1625-1626
Number of pages2
JournalBioinformatics
Volume30
Issue number11
DOIs
StatePublished - 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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