ArticlePLoS computational biology2013
Integrated module and gene-specific regulatory inference implicates upstream signaling networks.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer's and Parkinson's disease.GigaScience · 2026Article
- Article
- A network-based model of Aspergillus fumigatus elucidates regulators of development and defensive natural products of an opportunistic pathogen.Nucleic acids research · 2026Article
- Defining the role of β-cell IRE1α/XBP1 pathway and its gene regulatory network components in non-obese diabetic mice.Nature communications · 2025Article
- Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation.bioRxiv : the preprint server for biology · 2025Article
- TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.BMC genomics · 2025Article
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.BMC plant biology · 2024Article
- 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
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- A natural variation-based screen in mouse cells reveals USF2 as a regulator of the DNA damage response and cellular senescence.G3 (Bethesda, Md.) · 2023Article
- Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.Nature communications · 2023Article
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration.Bioinformatics (Oxford, England) · 2023Article
- Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula.BMC biology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.
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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.