ArticleGenome research2012
Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.
Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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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.
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Who cites it
68 citing papers in PubMed.
- Pleiotropy increases parallel selection signatures during adaptation from standing genetic variation.eLife · 2025Article
- Contribution of ECT2 to Tubulointerstitial Fibrosis in the Progression of Chronic Kidney Disease.Current medical science · 2024Article
- A dynamic regulome of shoot-apical-meristem-related homeobox transcription factors modulates plant architecture in maize.Genome biology · 2024Article
- Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin inPlants (Basel, Switzerland) · 2024Article
- Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.Bioinformatics (Oxford, England) · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Downregulation of a transcription factor associated with resistance to Bt toxin Vip3Aa in the invasive fall armyworm.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks.Communications biology · 2022Article
- Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework.Scientific reports · 2022Article
- Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging.Cells · 2022Review
- Behavioral genetics and genomics: Mendel's peas, mice, and bees.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Curation, inference, and assessment of a globally reconstructed gene regulatory network for Streptomyces coelicolor.Scientific reports · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination.Nucleic acids research · 2021Article
- Annotating the Insect Regulatory Genome.Insects · 2021Review
- Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators.Nature plants · 2021Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Developmental gene regulatory network connections predicted by machine learning from gene expression data alone.PloS one · 2021Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.
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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.