ArticleBioinformatics (Oxford, England)2013
iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.
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 92 papers.
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Who cites it
92 citing papers in PubMed, 155 citations in OpenAlex.
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.Data science in science · 2026Article
- A parametric survival model with bayesian structural equation based on multi-omics integration.BMC bioinformatics · 2025Article
- Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.Journal of personalized medicine · 2025Review
- Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases.JCI insight · 2025Article
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- Multi-omics Integrative Analysis for Incomplete Data Using WeightedJournal of agricultural, biological, and environmental statistics · 2025Article
- Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.bioRxiv : the preprint server for biology · 2024Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.Journal of the American Statistical Association · 2024Article
- Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity.Molecular and cellular endocrinology · 2023Review
- Tumor radiogenomics in gliomas with Bayesian layered variable selection.Medical image analysis · 2023Article
- Attempts to Understand Oral Mucositis in Head and Neck Cancer Patients through Omics Studies: A Narrative Review.International journal of molecular sciences · 2023Review
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.Statistica Sinica · 2023Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
- The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer.Frontiers in oral health · 2023Review
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
motivationAnalyzing data from multi-platform genomics experiments combined with patients' clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model.
resultsWe assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. AVAILABILITY: http://odin.mdacc.tmc.edu/∼vbaladan/. CONTACT: veera@mdanderson.org 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.