Abstract
Analysis procedures are needed to extract useful information from the large amount of gene expression data that is becoming available. This work describes a set of analytical tools and their application to yeast cell cycle data. The components of our approach are (1) a similarity measure that reduces the number of false positives, (2) a new clustering algorithm designed specifically for grouping gene expression patterns, and (3) an interactive graphical cluster analysis tool that allows user feedback and validation. We use the clusters generated by our algorithm to summarize genome-wide expression and to initiate supervised clustering of genes into biologically meaningful groups.
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Dates
Type | When |
---|---|
Created | 23 years, 1 month ago (July 26, 2002, 8 p.m.) |
Deposited | 3 years, 9 months ago (Nov. 18, 2021, 1:47 p.m.) |
Indexed | 13 hours, 59 minutes ago (Sept. 6, 2025, 3:54 p.m.) |
Issued | 25 years, 10 months ago (Nov. 1, 1999) |
Published | 25 years, 10 months ago (Nov. 1, 1999) |
Published Online | 25 years, 10 months ago (Nov. 1, 1999) |
Published Print | 25 years, 10 months ago (Nov. 1, 1999) |
@article{Heyer_1999, title={Exploring Expression Data: Identification and Analysis of Coexpressed Genes}, volume={9}, ISSN={1549-5469}, url={http://dx.doi.org/10.1101/gr.9.11.1106}, DOI={10.1101/gr.9.11.1106}, number={11}, journal={Genome Research}, publisher={Cold Spring Harbor Laboratory}, author={Heyer, Laurie J. and Kruglyak, Semyon and Yooseph, Shibu}, year={1999}, month=nov, pages={1106–1115} }