Machine Learning Methods for Epigenomic Analyis
Epigenetics concerns the study of heritable factors on gene
expression that are not directly coded in DNA sequences. Recent
studies has shown that one of the major epigenetics factor is
Nucleosomes spacial organization, where a nuclesome can be
considered as the basic unit of eukaryotic chromatin and physically
consists of about 150 bp of DNA wrapped around an histone proteins
core.
To measure nucleosome positions on a genomic scale both theoretical
and experimental approaches have been recently developed. Such
methodologies are mainly based on probabilistic models able to infer
nucleosome positions basing purely on genomic sequence information
or on genomic-scale hybridization data achieved by a tiled
microarray approach. In this tutorial recent developed machine
learning algorithms for nucleosome positioning will be analyzed
and discussed, giving also emphases to their comparison in terms
of computational complexity and classification accuracy.
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