Leveraging Global Gene Expression Patterns to Predict Expression of Unmeasured Genes

Authors

James Rudd, Dartmouth College
René A. Zelaya, Dartmouth College
Eugene Demidenko, Dartmouth CollegeFollow
Ellen L. Goode, Mayo Clinic
Casey S. Greene S. Greene, Dartmouth College
Jennifer A. Doherty, Dartmouth CollegeFollow

Document Type

Article

Publication Date

12-15-2015

Publication Title

BioMed Central Genomics

Department

Geisel School of Medicine

Abstract

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

DOI

10.1186/s12864-015-2250-5

Dartmouth Digital Commons Citation

Rudd, James; Zelaya, René A.; Demidenko, Eugene; Goode, Ellen L.; Greene, Casey S. Greene S.; and Doherty, Jennifer A., "Leveraging Global Gene Expression Patterns to Predict Expression of Unmeasured Genes" (2015). Dartmouth Scholarship. 600.
https://digitalcommons.dartmouth.edu/facoa/600