Hidden
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            arkov Models

Churchill, Gary A.

doi:10.1002/0470011815.b2a07028

Cited by 1 publication

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“…In addition, it is noted that identi ability issues will arise when a 01 C a 10 D 1 in a binary HMM (Churchill, 1998). It is also worth mentioning that the hidden state process will no longer be Markovian when a 01 C a 10 D 1.…”

Section: Hmms: Model De Nition and Notationmentioning

confidence: 99%

Use of Runs Statistics for Pattern Recognition in Genomic DNA Sequences

Cheung

2004

Journal of Computational Biology

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In this article, the use of the finite Markov chain imbedding (FMCI) technique to study patterns in DNA under a hidden Markov model (HMM) is introduced. With a vision of studying multiple runs-related statistics simultaneously under an HMM through the FMCI technique, this work establishes an investigation of a bivariate runs statistic under a binary HMM for DNA pattern recognition. An FMCI-based recursive algorithm is derived and implemented for the determination of the exact distribution of this bivariate runs statistic under an independent identically distributed (IID) framework, a Markov chain (MC) framework, and a binary HMM framework. With this algorithm, we have studied the distributions of the bivariate runs statistic under different binary HMM parameter sets; probabilistic profiles of runs are created and shown to be useful for trapping HMM maximum likelihood estimates (MLEs). This MLE-trapping scheme offers good initial estimates to jump-start the expectation-maximization (EM) algorithm in HMM parameter estimation and helps prevent the EM estimates from landing on a local maximum or a saddle point. Applications of the bivariate runs statistic and the probabilistic profiles in conjunction with binary HMMs for pattern recognition in genomic DNA sequences are illustrated via case studies on DNA bendability signals using human DNA data.

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Section: Hmms: Model De Nition and Notationmentioning

confidence: 99%

Use of Runs Statistics for Pattern Recognition in Genomic DNA Sequences

Cheung

2004

Journal of Computational Biology

View full text Add to dashboard Cite

show abstract

Hidden M arkov Models

Cited by 1 publication

References 20 publications

Use of Runs Statistics for Pattern Recognition in Genomic DNA Sequences

Use of Runs Statistics for Pattern Recognition in Genomic DNA Sequences

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