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

Comments

Originally published at doi: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.

Is Part Of

VCU Biostatistics Publications

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.

gb-2007-8-9-r187-s1.xls (776 kB)
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.

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