ArticleBioinformatics (Oxford, England)2018
runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- Pangenome analysis of nine soybean cyst nematode genomes reveals hidden variation contributing to diversity and adaptation.BMC genomics · 2026Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- A parameter free relative density based biclustering method for identifying non-linear feature relations.Heliyon · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Biclustering for Epi-Transcriptomic Co-functional Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data.Frontiers in genetics · 2021Article
- EBIC: an open source software for high-dimensional and big data analyses.Bioinformatics (Oxford, England) · 2019Article
- Mining conditions specific hub genes from RNA-Seq gene-expression data via biclustering and their application to drug discovery.IET systems biology · 2019Article
- iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Motivation: Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far. Results: In this paper we introduce a Bioconductor package called runibic with parallel implementation of UniBic. For the convenience the algorithm was reimplemented, parallelized and wrapped within an R package called runibic. The package includes: (i) a couple of times faster parallel version of the original sequential algorithm, (ii) much more efficient memory management, (iii) modularity which allows to build new methods on top of the provided one and (iv) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSet and biclust. Availability and implementation: The package is implemented in R and is available from Bioconductor (starting from version 3.6) at the following URL http://bioconductor.org/packages/runibic with installation instructions and tutorial. Supplementary information: Supplementary data are available at Bioinformatics online.
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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 Socratic graph.