ArticleBioinformatics (Oxford, England)2010
FABIA: factor analysis for bicluster acquisition.
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 101 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- iBBiG: iterative binary bi-clustering of gene sets.Bioinformatics (Oxford, England) · 2012Pooled it
- NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.Bioinformatics (Oxford, England) · 2026Article
- Multi-Omic Bicluster Association Analysis (MOBAA)-a tool for identifying population subgroups with distinct multi-omics molecular profiles.Bioinformatics advances · 2026Article
- MNetClass: a control-free microbial network clustering framework for identifying central subcommunities across ecological niches.mSystems · 2025Article
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Splicing regulatory dynamics for precision analysis and treatment of heterogeneous leukemias.Science translational medicine · 2025Article
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- SUMO: an R package for simulating multi-omics data for methods development and testing.Bioinformatics advances · 2025Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
- TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.Briefings in bioinformatics · 2024Article
- Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.Briefings in bioinformatics · 2024Article
- A parameter free relative density based biclustering method for identifying non-linear feature relations.Heliyon · 2024Article
- SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.bioRxiv : the preprint server for biology · 2024Article
- A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.Biometrics · 2024Article
- Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.Journal of Intelligence · 2024Article
- Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2024Article
41 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationBiclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques.
resultsOn 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches. AVAILABILITY: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
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.