ArticleBioinformatics (Oxford, England)2021
DCI: learning causal differences between gene regulatory networks.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal hypothesis generation using conditional SHAP.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA.The annals of applied statistics · 2025Article
- Adapting systems biology to address the complexity of human disease in the single-cell era.Nature reviews. Genetics · 2025Review
- Causal machine learning for single-cell genomics.Nature genetics · 2025Review
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm.Molecular biotechnology · 2024Article
- A dynamical perspective: moving towards mechanism in single-cell transcriptomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
- Gene regulatory network inference based on causal discovery integrating with graph neural network.Quantitative biology (Beijing, China) · 2023Article
- Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine.JMIR medical informatics · 2023Article
- Causal Structure Learning: A Combinatorial Perspective.Foundations of computational mathematics (New York, N.Y.) · 2022Article
- The use of machine learning to discover regulatory networks controlling biological systems.Molecular cell · 2022Review
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Authors and funding
3 authors.
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Abstract
summaryDesigning interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. AVAILABILITY AND IMPLEMENTATION: Python package freely available at http://uhlerlab.github.io/causaldag/dci. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.