ArticleBioinformatics (Oxford, England)2022
Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- High-quality sika deer omics data and integrative analysis reveal genic and cellular regulation of antler regeneration.Genome research · 2025Article
- Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules.BME frontiers · 2025Article
- Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions inFrontiers in microbiology · 2025Article
- Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to MgComputational and structural biotechnology journal · 2024Article
- A transcriptomic atlas of acute stress response to low pH in multipleMicrobiology spectrum · 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
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationTranscriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying gene regulatory network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression.
resultsIn this article, we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN+P datasets as benchmarks. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts. AVAILABILITY AND IMPLEMENTATION: The code and instructions for data acquisition and full reproduction of our results are available at https://github.com/AntoinePassemiers/PORTIA-Manuscript. PORTIA is available on PyPI as a Python package (portia-grn). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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