ArticleBMC bioinformatics2017
A systematic comparative evaluation of biclustering techniques.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 132 citations in OpenAlex.
- NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.Bioinformatics (Oxford, England) · 2026Article
- A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data screening strategy.PLoS computational biology · 2026Article
- CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.PLoS computational biology · 2026Article
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- CellSP enables module discovery and visualization for subcellular spatial transcriptomics data.Communications biology · 2025Article
- Single-molecule localisation microscopy (SMLM) is feasible in human and animal formalin fixed paraffin embedded (FFPE) tissues in medical renal disease.Journal of clinical pathology · 2025Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics.Nucleic acids research · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox.BMC infectious diseases · 2024Article
- Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.Journal of Intelligence · 2024Article
- Robust knowledge-guided biclustering for multi-omics data.Briefings in bioinformatics · 2023Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Approaches in Gene Coexpression Analysis in Eukaryotes.Biology · 2022Review
- Biclustering fMRI time series: a comparative study.BMC bioinformatics · 2022Article
- MoSBi: Automated signature mining for molecular stratification and subtyping.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Comparison of sparse biclustering algorithms for gene expression datasets.Briefings in bioinformatics · 2021Article
- Decompose Boolean Matrices with Correlation Clustering.Entropy (Basel, Switzerland) · 2021Article
- Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.Medical & biological engineering & computing · 2021Article
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Authors and funding
2 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets.
resultsWe conducted a broader comparative study involving seventeen algorithms, which were run on three synthetic data collections and two real data collections with a more representative number of datasets. For the experiments with synthetic data, five different experimental scenarios were studied: different levels of noise, different numbers of implanted biclusters, different levels of symmetric bicluster overlap, different levels of asymmetric bicluster overlap and different bicluster sizes, for which the results were assessed with more suitable external measures. For the experiments with real datasets, the results were assessed by gene set enrichment and clustering accuracy.
conclusionsWe observed that each algorithm achieved satisfactory results in part of the biclustering tasks in which they were investigated. The choice of the best algorithm for some application thus depends on the task at hand and the types of patterns that one wants to detect.
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