ArticleNature communications2025
Large-scale causal discovery using interventional data sheds light on gene network structure in k562 cells.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- MR2G: A novel framework for causal network inference using GWAS summary data.PLoS genetics · 2026Article
- Deriving Mendelian Randomization-Based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Airqtl dissects cell state-specific causal gene regulatory networks with efficient single-cell eQTL mapping.Nature communications · 2025Article
- Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.medRxiv : the preprint server for health sciences · 2024Article
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5 authors.
Funding
Abstract
Inference of directed biological networks is an important but notoriously challenging problem. The recent proliferation of large-scale CRISPR perturbation data provides a new opportunity to tackle this problem by leveraging the transcriptional response to the presence of a gene-targeting guide. Here, we introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre discovers a network with small-world and scale-free properties. We integrate our network estimate with external data, finding relationships between gene eigencentrality and both measures of gene essentiality and gene expression heritability. Our analysis helps to elucidate the structure of networks that may underlie complex traits.
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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.