ArticleBioinformatics (Oxford, England)2010
Discovering transcriptional modules by Bayesian data integration.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
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- Multiple kernel learning for integrative consensus clustering of omic datasets.Bioinformatics (Oxford, England) · 2020Article
- MONET: Multi-omic module discovery by omic selection.PLoS computational biology · 2020Article
- Treatment with dimethyl fumarate reduces the formation and rupture of intracranial aneurysms: Role of Nrf2 activation.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2020Article
- Multi-omics Data Integration, Interpretation, and Its Application.Bioinformatics and biology insights · 2020Review
- Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method.IET systems biology · 2019Article
- Integration of molecular features with clinical information for predicting outcomes for neuroblastoma patients.Biology direct · 2019Article
- Multi-omic and multi-view clustering algorithms: review and cancer benchmark.Nucleic acids research · 2018Review
- MicroRNA and cellular targets profiling reveal miR-217 and miR-576-3p as proviral factors during Oropouche infection.PLoS neglected tropical diseases · 2018Article
- Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis.BMC bioinformatics · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationWe present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets.
resultsWe find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs. AVAILABILITY: If interested in the code for the work presented in this article, please contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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