Combined clustering models for the analysis of gene expression

Angelova, Maia and Ellman, Jeremy (2010) Combined clustering models for the analysis of gene expression. Physics of Atomic Nuclei, 73 (2). pp. 242-246. ISSN 1063-7788

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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.

Item Type: Article
Uncontrolled Keywords: gene expression
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: EPrint Services
Date Deposited: 03 Mar 2010 10:52
Last Modified: 13 Oct 2019 00:30

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