ArticleCell reports2018
Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks.
Article in Cell reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 42 citations in OpenAlex.
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.Genome biology · 2024Article
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
- Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression.bioRxiv : the preprint server for biology · 2023Article
- MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.Bioinformatics (Oxford, England) · 2023Article
- High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.Bioinformatics (Oxford, England) · 2022Article
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.mSystems · 2020Article
- Article
- Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.Genome research · 2019Article
- Multi-study inference of regulatory networks for more accurate models of gene regulation.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.
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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.