Extracting conserved gene expression motifs from gene expression data.

T M Murali; Simon Kasif

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PubMed · 12603019

Extracting conserved gene expression motifs from gene expression data.

T M Murali·Simon Kasif

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Abstract

We propose a representation for gene expression data called conserved gene expression motifs or XMOTIFs. A gene's expression level is conserved across a set of samples if the gene is expressed with the same abundance in all the samples. A conserved gene expression motif is a subset of genes that is simultaneously conserved across a subset of samples. We present a computational technique to discover large conserved gene motifs that cover all the samples and classes in the data. When applied to published data sets representing different cancers or disease outcomes, our algorithm constructs XMOTIFS that distinguish between the various classes.

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Algorithms Colonic Neoplasms Databases, Genetic Gene Expression Profiling Humans Leukemia, Myeloid, Acute Lymphoma, B-Cell Models, Genetic Oligonucleotide Array Sequence Analysis Precursor Cell Lymphoblastic Leukemia-Lymphoma Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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T M Murali, Simon Kasif. 2003. Extracting conserved gene expression motifs from gene expression data.. https://pubmed.ncbi.nlm.nih.gov/12603019/

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