ArticleBioinformatics (Oxford, England)2017
Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.Frontiers in microbiology · 2023Article
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
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4 authors.
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Abstract
motivationGrowth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.
resultsWe first demonstrate that detecting such 'co-fit' gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast. AVAILABILITY AND IMPLEMENTATION: Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. CONTACT: xin.gao@kaust.edu.sa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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