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Updated: Jul 11, 2026

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR
Published on: July 30, 2011
The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data
Gabriel S Eichler1, Mark Reimers, David Kane
1Genomics and Bioinformatics Groups, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
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.
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