ArticleBioinformatics (Oxford, England)2012
Bayesian correlated clustering to integrate multiple datasets.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
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Who cites it
114 citing papers in PubMed.
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- A fast integrative clustering and feature selection approach for high-dimensional multiview data.Statistical methods in medical research · 2026Article
- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- DSCC: disease subtyping using spectral clustering and community detection from consensus networks.Briefings in bioinformatics · 2025Article
- VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.Bioinformatics advances · 2025Article
- Multi-omics Integrative Analysis for Incomplete Data Using WeightedJournal of agricultural, biological, and environmental statistics · 2025Article
- Cross-Cohort Mixture Analysis: A Data Integration Approach With Applications on Gestational Age and DNA-Methylation-Derived Gestational Age Acceleration Metrics.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.Environment international · 2024Article
- ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii.PLoS computational biology · 2024Article
- Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.Methodology : European journal of research methods for the behavioral & social sciences · 2024Article
- Article
- Integrating omics atlas in health informatics system design-an opinion article.Frontiers in digital health · 2024Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- Cancer survival prediction by learning comprehensive deep feature representation for multiple types of genetic data.BMC bioinformatics · 2023Article
- Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.BMC medicine · 2023Review
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.Axioms · 2023Article
- Identification of functional gene modules by integrating multi-omics data and known molecular interactions.Frontiers in genetics · 2023Article
54 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
motivationThe integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.
resultsUsing a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.
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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.