ArticleBioinformatics (Oxford, England)2018
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Splicing regulatory dynamics for precision analysis and treatment of heterogeneous leukemias.Science translational medicine · 2025Article
- Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.Briefings in bioinformatics · 2024Article
- TransBic: bucket trend-preserving biclustering for finding local and interpretable expression patterns.Briefings in bioinformatics · 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
- A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis.BMC bioinformatics · 2022Article
- Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers.Molecules (Basel, Switzerland) · 2021Article
- REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data.Frontiers in genetics · 2021Article
- QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data.Bioinformatics (Oxford, England) · 2020Article
- EBIC: an open source software for high-dimensional and big data analyses.Bioinformatics (Oxford, England) · 2019Article
- A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions.Methods (San Diego, Calif.) · 2019Article
- Scalable biclustering - the future of big data exploration?GigaScience · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Motivation: Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. Results: In this paper, a novel biclustering algorithm based on evolutionary computation, a sub-field of artificial intelligence, is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units. We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. Availability and implementation: EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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