ArticleGenome biology2007
The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data.
Article in Genome biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.Bioinformatics (Oxford, England) · 2020Article
- Application of random forest based approaches to surface-enhanced Raman scattering data.Scientific reports · 2020Article
- A system-level pathway-phenotype association analysis using synthetic feature random forest.Genetic epidemiology · 2014Article
- Random forests for genomic data analysis.Genomics · 2012Review
- Investigating the effect of paralogs on microarray gene-set analysis.BMC bioinformatics · 2011Article
- Identification of functional modules that correlate with phenotypic difference: the influence of network topology.Genome biology · 2010Article
- Machine learning approach identifies new pathways associated with demyelination in a viral model of multiple sclerosis.Journal of cellular and molecular medicine · 2010Article
- Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family.BMC medical genomics · 2009Article
- Human urinary metabolomic profile of PPARalpha induced fatty acid beta-oxidation.Journal of proteome research · 2009Article
- Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data.BMC medical genomics · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.