ArticleNucleic acids research2017
A prior-based integrative framework for functional transcriptional regulatory network inference.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 43 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
43 citing papers in PubMed, 78 citations in OpenAlex.
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Article
- MINI-EX Version 2: Cell-Type-Specific Gene Regulatory Network Inference Using an Integrative Single-Cell Transcriptomics Approach.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation.bioRxiv : the preprint server for biology · 2025Article
- Leveraging transcription factor physical proximity for enhancing gene regulation inference.Bioinformatics (Oxford, England) · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.Briefings in bioinformatics · 2024Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.Bioinformatics (Oxford, England) · 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
- CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.PLoS computational biology · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.Bioinformatics advances · 2024Article
- SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction.bioRxiv : the preprint server for biology · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.Nucleic acids research · 2023Article
- Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer.NPJ systems biology and applications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.
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.