ArticleBriefings in bioinformatics2013
A comparative analysis of biclustering algorithms for gene expression data.
Article in Briefings in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.Bioinformatics (Oxford, England) · 2026Article
- A personalized reinforcement learning recommendation algorithm using bi-clustering techniques.PloS one · 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
- Uncovering hidden gene-trait patterns through biclustering analysis of the UK Biobank.bioRxiv : the preprint server for biology · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus.Journal of integrative bioinformatics · 2024Article
- Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.Journal of Intelligence · 2024Article
- G-bic: generating synthetic benchmarks for biclustering.BMC bioinformatics · 2023Article
- ARBic: an all-round biclustering algorithm for analyzing gene expression data.NAR genomics and bioinformatics · 2023Article
- Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.Computational and structural biotechnology journal · 2023Article
- Identification of functional gene modules by integrating multi-omics data and known molecular interactions.Frontiers in genetics · 2023Article
- Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.Frontiers in microbiology · 2023Article
- Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images.Journal of digital imaging · 2022Article
- Article
- A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis.BMC bioinformatics · 2022Article
- 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
- Article
10 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.
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