Sparsely correlated hidden Markov models with application to genome-wide location studies.

ABSTRACT:

Motivation

Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable.

Results

We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the

SUBMITTER: Choi H 

PROVIDER: S-EPMC3582268 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature