The LeFE Algorithm: Embracing the Complexity of Gene Expression in the Interpretation of Microarray Data
Eichler, Gabriel S.
Weinstein, John N.
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Citation (published version)Eichler, Gabriel S, Mark Reimers, David Kane, John N Weinstein. "The LeFE Algorithm: Embracing the Complexity of Gene Expression in the Interpretation of Microarray Data" Genome Biology 8(9):R187. (2007)
The LeFE algorithm has been developed to address the complex, non-linear regulation of gene expression. 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.
RightsCopyright 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.