ArticleBioinformatics (Oxford, England)2018
piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The Physiology Of the WEight Reduced State (POWERS) study: overview and study design.International journal of obesity (2005) · 2026Review
- DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.Briefings in bioinformatics · 2024Article
- Article
- Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis.Respiratory research · 2023Article
- Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets.Patterns (New York, N.Y.) · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.Frontiers in genetics · 2021Article
- Protecting the lungs but hurting the kidneys: causal inference study for the risk of ventilation-induced kidney injury in ARDS.Annals of translational medicine · 2020Article
- CausalMGM: an interactive web-based causal discovery tool.Nucleic acids research · 2020Article
- Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models.Thorax · 2019Article
- Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.Bioinformatics (Oxford, England) · 2019Article
- PARP1 rs1805407 Increases Sensitivity to PARP1 Inhibitors in Cancer Cells Suggesting an Improved Therapeutic Strategy.Scientific reports · 2019Article
- Inferring Interaction Networks From Multi-Omics Data.Frontiers in genetics · 2019Review
- Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2019Article
- A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Motivation: Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential applications in systems medicine, the problems of which often involve high-dimensional data. Results: We present a new method, piMGM, which can learn with accuracy the structure of probabilistic graphs over mixed data by appropriately incorporating priors from multiple experts with different degrees of reliability. We show that piMGM accurately scores the reliability of prior information from a given expert even at low sample sizes. The reliability scores can be used to determine active pathways in healthy and disease samples. We tested piMGM on both simulated and real data from TCGA, and we found that its performance is not affected by unreliable priors. We demonstrate the applicability of piMGM by successfully using prior information to identify pathway components that are important in breast cancer and improve cancer subtype classification. Availability and implementation: http://www.benoslab.pitt.edu/manatakisECCB2018.html. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.