ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data | Zendy

Maria Osmala | Zendy; Gökçen Eraslan | Zendy; Harri Lähdesmäki | Zendy

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ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data

Author(s) -

Maria Osmala,

Gökçen Eraslan,

Harri Lähdesmäki

Publication year - 2022

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac444

Subject(s) - epigenetics , cluster analysis , chromatin , enhancer , computational biology , biology , encode , epigenomics , computer science , chromosome conformation capture , genomics , genome , genetics , dna methylation , gene , artificial intelligence , gene expression

Research on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements.

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