Parallel computation and visualization tools for codetermination analysis of multivariate gene expression relations

Edward B. Suh, Edward R. Dougherty, Seungchan Kim, Michael L. Bittner, Yidong Chen, Daniel E. Russ, Robert L. Martino

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A key goal of functional genomics is to develop methods for determining ways in which individual actions of genes are integrated in the cell. Kim et al. have proposed using the nonlinear coefficient of determination for finding associations between genes (Kim et al., 2000a; Kim et al., 2000b; Dougherty et al., 2000). The method assesses the codetermination of gene transcriptional states based on statistical evaluation of reliably informative subsets of data derived from large-scale gene-expression measurements. It measures the degree to which the transcriptional levels of a small gene set can be used to predict the transcriptional levels of a small gene set can be used to predict the transcriptional state of a target gene in excess of the predictive capability of the mean level of the target. Conditions besides transcriptional features can be used as predictive elements, thus broadening the information that can be evaluated in modelling biological regulation.

Original languageEnglish (US)
Title of host publicationComputational and Statistical Approaches to Genomics
PublisherSpringer US
Pages297-310
Number of pages14
Volume9780387262871
ISBN (Electronic)9780387262888
ISBN (Print)0387262873, 9780387262871
DOIs
StatePublished - Jan 1 2006
Externally publishedYes

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ASJC Scopus subject areas

  • Medicine(all)

Cite this

Suh, E. B., Dougherty, E. R., Kim, S., Bittner, M. L., Chen, Y., Russ, D. E., & Martino, R. L. (2006). Parallel computation and visualization tools for codetermination analysis of multivariate gene expression relations. In Computational and Statistical Approaches to Genomics (Vol. 9780387262871, pp. 297-310). Springer US. https://doi.org/10.1007/0-387-26288-1_15