ReviewGenomics & informatics2014
A review of three different studies on hidden markov models for epigenetic problems: a computational perspective.
Review in Genomics & informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.Bioinformatics (Oxford, England) · 2024Article
- Reliable genotyping of recombinant genomes using a robust hidden Markov model.Plant physiology · 2023Article
- Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis.Journal of immunology research · 2015Review
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2 authors.
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Abstract
Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.
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