ArticlePLoS computational biology2017
Learning causal networks with latent variables from multivariate information in genomic data.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia.Nature communications · 2026Article
- CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication.Nucleic acids research · 2025Article
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
- CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data.eLife · 2025Article
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients.iScience · 2024Article
- A multistep computational approach reveals a neuro-mesenchymal cell population in the embryonic hematopoietic stem cell niche.Development (Cambridge, England) · 2024Article
- Granger causality analysis for calcium transients in neuronal networks, challenges and improvements.eLife · 2023Article
- Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging.Cells · 2022Review
- Interactive exploration of a global clinical network from a large breast cancer cohort.NPJ digital medicine · 2022Article
- Article
- Single-Cell RNA-Seq Reveals the Promoting Role of Ferroptosis Tendency During Lung Adenocarcinoma EMT Progression.Frontiers in cell and developmental biology · 2021Article
- Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers.PLoS computational biology · 2021Article
- Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms.Frontiers in genetics · 2021Article
- Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations.iScience · 2020Article
- Learning clinical networks from medical records based on information estimates in mixed-type data.PLoS computational biology · 2020Article
- OHNOLOGS v2: a comprehensive resource for the genes retained from whole genome duplication in vertebrates.Nucleic acids research · 2020Article
- Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells.Acta neuropathologica communications · 2019Article
- Executable pathway analysis using ensemble discrete-state modeling for large-scale data.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.
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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.