Document Type
Article
Original Publication Date
2007
Journal/Book/Conference Title
Genome Biology
Volume
8
Issue
R187
DOI of Original Publication
10.1186/gb-2007-8-9-r187
Date of Submission
August 2014
Abstract
Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.
Recommended Citation
© 2007 Eichler et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Is Part Of
VCU Biostatistics Publications
Complete results from the smoker/never-smoker demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.
gb-2007-8-9-r187-s2.xls (746 kB)
Complete results from the breast cancer demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.
gb-2007-8-9-r187-s3.xls (664 kB)
Complete results from the gefitinib demonstration, including gene categories ranked by LeFE computed median permutation t-test P value and individual gene importance scores.
Comments
Originally published at doi:10.1186/gb-2007-8-9-r187.