SynthesisBioinformatics (Oxford, England)2012
iBBiG: iterative binary bi-clustering of gene sets.
Synthesis 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 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Targeted Sequencing and Meta-Analysis of Preterm Birth.PloS one · 2016Pooled it
- Trial
- Bioinformatic Analyzes of the Association Between Upregulated Expression ofFrontiers in medicine · 2022Article
- PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data.Journal of biosciences · 2021Article
- TERT mutations correlate with higher TMB value and unique tumor microenvironment and may be a potential biomarker for anti-CTLA4 treatment.Cancer medicine · 2020Article
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
- runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.Bioinformatics (Oxford, England) · 2018Article
- The Immune Landscape of Cancer.Immunity · 2018Article
- Semantic biclustering for finding local, interpretable and predictive expression patterns.BMC genomics · 2017Article
- Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data.Bioinformatics (Oxford, England) · 2017Article
- Dimension reduction techniques for the integrative analysis of multi-omics data.Briefings in bioinformatics · 2016Review
- Composition and temporal stability of the gut microbiota in older persons.The ISME journal · 2016Article
- Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.Bioinformatics (Oxford, England) · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
motivationMeta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.
resultsWe transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set-phenotype association that predicted tumor metastases within tumor subtypes. AVAILABILITY: Implemented in the Bioconductor package iBBiG CONTACT: aedin@jimmy.harvard.edu.
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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.