Combined clustering models for the analysis of gene expression

Maia Angelova, Jeremy Ellman

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)242-246
JournalPhysics of Atomic Nuclei
Issue number2
Publication statusPublished - 2010


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