ArticlePloS one2010
Inferring regulatory networks from expression data using tree-based methods.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,081 papers, 1 of them a synthesis that pooled it.
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Who cites it
1,081 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Key Differentially Expressed Genes inInternational journal of molecular sciences · 2025Pooled it
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Context matters: coordinated transcriptional regulation and root plasticity under multinutrient conditions.The New phytologist · 2026Article
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Multi-omics integration and artificial intelligence for the conservation and utilization of local chicken genetic resources.Poultry science · 2026Review
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
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- Single-Nucleus Transcriptomes of Lacrimal Gland Adenoid Cystic Carcinoma With Different Growth Patterns.Head & neck · 2026Article
- From knowledge graph to topological data analysis: a novel framework to analyze gene regulatory networks for tomato-multi-pathogen interactions.The New phytologist · 2026Article
- Constructing cell-type-specific gene regulatory networks from cell-cell communication and a global gene regulatory network.Briefings in bioinformatics · 2026Article
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- CHG Hypomethylation of BoCYP83A1 May Contribute to Methyl Jasmonate-Induced Sulforaphane Accumulation in Broccoli.Plant, cell & environment · 2026Article
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- SignifiKANTE: efficient P-value computation for gene regulatory networks.Bioinformatics (Oxford, England) · 2026Article
- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference.International journal of molecular sciences · 2026Article
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- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
1,021 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.
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