Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 620, 405-16

Hidden Markov Model and Its Applications in Motif Findings


Hidden Markov Model and Its Applications in Motif Findings

Jing Wu et al. Methods Mol Biol.


Hidden Markov models have wide applications in pattern recognition. In genome sequence analysis, hidden Markov models (HMMs) have been applied to the identification of regions of the genome that contain regulatory information, i.e., binding sites. In higher eukaryotes, the regulatory information is organized into modular units called cis-regulatory modules. Each module contains multiple binding sites for a specific combination of several transcription factors. In this chapter, we gave a brief review of hidden Markov models, standard algorithms from HMM, and their applications to motif findings. We then introduce the application of HMM to a complex system in which an HMM is combined with Bayesian inference to identify transcription factor binding sites and cis-regulatory modules.

Similar articles

See all similar articles

Cited by 4 articles

LinkOut - more resources