Combined clustering models for the analysis of gene expression

Autor:	Maia Angelova, Jeremy Ellman
Rok vydání:	2010
Předmět:	Gene expression profiling Physics Regulation of gene expression Nuclear and High Energy Physics Similarity (network science) Gene expression Computational biology Cluster analysis Gene Functional genomics Atomic and Molecular Physics and Optics Function (biology)
Zdroj:	Physics of Atomic Nuclei. 73:242-246
ISSN:	1562-692X 1063-7788
Popis:	Clustering has become one of the fundamental tools for analyzing gene expression and producing gene classifications. Clustering models enable finding patterns of similarity in order to understand gene function, gene regulation, cellular processes and sub-types of cells. The clustering results however have to be combined with sequence data or knowledge about gene functionality in order to make biologically meaningful conclusions. In this work, we explore a new model that integrates gene expression with sequence or text information.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f625e26a480d72b30c0a0983fbeedafe https://doi.org/10.1134/s1063778810020067 Zobrazit plný text záznamu Plný text ve formátu PDF