ArticleBioinformatics (Oxford, England)2013
Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- MPAC: a computational framework for inferring pathway activities from multi-omic data.Bioinformatics (Oxford, England) · 2025Article
- mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.bioRxiv : the preprint server for biology · 2024Article
- mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.Bioinformatics advances · 2024Article
- ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis.Molecular & cellular proteomics : MCP · 2020Article
- Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer.Cancer cell · 2020Article
- Exploiting Interdata Relationships in Next-generation Proteomics Analysis.Molecular & cellular proteomics : MCP · 2019Review
- Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.PLoS computational biology · 2019Article
- Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers.Endocrine-related cancer · 2019Review
- Systems Biology Analyses Show Hyperactivation of Transforming Growth Factor-β and JNK Signaling Pathways in Esophageal Cancer.Gastroenterology · 2019Article
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- Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma.Cancer cell · 2017Article
- minepath.org: a free interactive pathway analysis web server.Nucleic acids research · 2017Article
- Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach.Non-coding RNA · 2017Article
- Article
- MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.PLoS computational biology · 2016Article
- Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification.Oncotarget · 2016Article
- Learning mixed graphical models with separate sparsity parameters and stability-based model selection.BMC bioinformatics · 2016Article
- Article
- Sharing information to reconstruct patient-specific pathways in heterogeneous diseases.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
unlabelledHigh-dimensional '-omics' profiling provides a detailed molecular view of individual cancers; however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237-i245), a pathway analysis method for combining multiple '-omics' data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature. Using genomic and mRNA expression data from 1936 samples in The Cancer Genome Atlas (TCGA) cohort, we learned interactions that provided support for and relative strength of 7138 (78%) of the curated links. Gene set enrichment found that genes involved in the strongest interactions were significantly enriched for transcriptional regulation, apoptosis, cell cycle regulation and response to tumor cells. Within the TCGA breast cancer cohort, we assessed different interaction strengths between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. PARADIGM with the Naive Bayesian assumption produced gene activity predictions that, when clustered, found groups of patients with better separation in survival than both the original version of PARADIGM and a version without the assumption. We found that this Naive Bayes assumption was valid for the vast majority of co-regulators, indicating that most co-regulators act independently on their shared target. AVAILABILITY: http://paradigm.five3genomics.com. 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.