ArticleScientific reports2016
UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.Bioinformatics (Oxford, England) · 2026Article
- TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.Briefings in bioinformatics · 2024Article
- A parameter free relative density based biclustering method for identifying non-linear feature relations.Heliyon · 2024Article
- 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
- ARBic: an all-round biclustering algorithm for analyzing gene expression data.NAR genomics and bioinformatics · 2023Article
- MoSBi: Automated signature mining for molecular stratification and subtyping.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.Medical & biological engineering & computing · 2021Article
- REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data.Frontiers in genetics · 2021Article
- Mining conditions specific hub genes from RNA-Seq gene-expression data via biclustering and their application to drug discovery.IET systems biology · 2019Article
- COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification.Journal of biosciences · 2019Article
- runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.Bioinformatics (Oxford, England) · 2018Article
- EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.Bioinformatics (Oxford, England) · 2018Article
- A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules.Scientific reports · 2017Article
- Network-aided Bi-Clustering for discovering cancer subtypes.Scientific reports · 2017Article
- BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.PloS one · 2016Article
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4 authors.
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Abstract
Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed.
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