ArticleBioinformatics (Oxford, England)2019
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- The Physiology Of the WEight Reduced State (POWERS) study: overview and study design.International journal of obesity (2005) · 2026Review
- New causal discovery algorithm over censored variables identifies subtype-specific drivers of breast cancer progression.GigaScience · 2026Article
- DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal hypothesis generation using conditional SHAP.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Plasma Levels of Soluble ST2 Reflect Extrapulmonary Organ Dysfunction and Predict Outcomes in Acute Respiratory Failure.Critical care medicine · 2025Observational
- Epstein-Barr Virus Seropositivity, Immune Dysregulation, and Mortality in Pediatric Sepsis.JAMA network open · 2025Article
- Identifying pathways to cardiovascular mortality by causal graphical models and mediation analysis among hypertensive patients: insights from a prospective study.Journal of translational medicine · 2025Article
- Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.PLoS medicine · 2024Article
- A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-associated hepatitis.Hepatology communications · 2024Article
- Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes.bioRxiv : the preprint server for biology · 2024Article
- Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients.iScience · 2024Article
- Pre-vaccination transcriptomic profiles of immune responders to the MUC1 peptide vaccine for colon cancer prevention.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Ideal cardiovascular health metrics have better identification of arthritis.BMC public health · 2024Article
- Pre-vaccination transcriptomic profiles of immune responders to the MUC1 peptide vaccine for colon cancer prevention.Frontiers in immunology · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning.The Journal of thoracic and cardiovascular surgery · 2023Article
- Integrated BATF transcriptional network regulates suppressive intratumoral regulatory T cells.Science immunology · 2023Article
- Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.BMC medicine · 2023Article
- The plasma degradome reflects later development of NASH fibrosis after liver transplant.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
motivationIntegration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data.
resultsIn this paper, we present and test new methods for finding directed graphs over mixed data types (continuous and discrete variables). We used this new algorithm, CausalMGM, to identify variables directly linked to disease diagnosis and progression in various multi-modal datasets, including clinical datasets from chronic obstructive pulmonary disease (COPD). COPD is the third leading cause of death and a major cause of disability and thus determining the factors that cause longitudinal lung function decline is very important. Applied on a COPD dataset, mixed graphical models were able to confirm and extend previously described causal effects and provide new insights on the factors that potentially affect the longitudinal lung function decline of COPD patients. AVAILABILITY AND IMPLEMENTATION: The CausalMGM package is available on http://www.causalmgm.org. 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.