ArticleBMC systems biology2013
ENNET: inferring large gene regulatory networks from expression data using gradient boosting.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data.PLoS computational biology · 2024Article
- DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.Briefings in bioinformatics · 2024Article
- SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.Bioinformatics advances · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction.bioRxiv : the preprint server for biology · 2023Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.Bioinformatics (Oxford, England) · 2022Article
- Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN.Frontiers in oncology · 2022Article
- KBoost: a new method to infer gene regulatory networks from gene expression data.Scientific reports · 2021Article
- PFBNet: a priori-fused boosting method for gene regulatory network inference.BMC bioinformatics · 2020Article
- D3GRN: a data driven dynamic network construction method to infer gene regulatory networks.BMC genomics · 2019Article
- Article
- PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation.Frontiers in plant science · 2019Article
- RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.Nucleic acids research · 2018Article
- BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data.BMC systems biology · 2018Article
- Characterization of DNA Methylation Associated Gene Regulatory Networks During Stomach Cancer Progression.Frontiers in genetics · 2018Article
- SCENIC: single-cell regulatory network inference and clustering.Nature methods · 2017Article
- Gene regulatory network inference using PLS-based methods.BMC bioinformatics · 2016Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe regulation of gene expression by transcription factors is a key determinant of cellular phenotypes. Deciphering genome-wide networks that capture which transcription factors regulate which genes is one of the major efforts towards understanding and accurate modeling of living systems. However, reverse-engineering the network from gene expression profiles remains a challenge, because the data are noisy, high dimensional and sparse, and the regulation is often obscured by indirect connections.
resultsWe introduce a gene regulatory network inference algorithm ENNET, which reverse-engineers networks of transcriptional regulation from a variety of expression profiles with a superior accuracy compared to the state-of-the-art methods. The proposed method relies on the boosting of regression stumps combined with a relative variable importance measure for the initial scoring of transcription factors with respect to each gene. Then, we propose a technique for using a distribution of the initial scores and information about knockouts to refine the predictions. We evaluated the proposed method on the DREAM3, DREAM4 and DREAM5 data sets and achieved higher accuracy than the winners of those competitions and other established methods.
conclusionsSuperior accuracy achieved on the three different benchmark data sets shows that ENNET is a top contender in the task of network inference. It is a versatile method that uses information about which gene was knocked-out in which experiment if it is available, but remains the top performer even without such information. ENNET is available for download from https://github.com/slawekj/ennet under the GNU GPLv3 license.
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