ArticleJournal of computational biology : a journal of computational molecular cell biology2012
Biclustering of linear patterns in gene expression data.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2012. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Pan-Cancer Analysis Reveals the Prognostic Potential of the THAP9/THAP9-AS1 Sense-Antisense Gene Pair in Human Cancers.Non-coding RNA · 2022Article
- Gene co-expression analysis for functional classification and gene-disease predictions.Briefings in bioinformatics · 2018Article
- Generalized correlation measure using count statistics for gene expression data with ordered samples.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Identifying a bicluster, or submatrix of a gene expression dataset wherein the genes express similar behavior over the columns, is useful for discovering novel functional gene interactions. In this article, we introduce a new algorithm for finding biClusters with Linear Patterns (CLiP). Instead of solely maximizing Pearson correlation, we introduce a fitness function that also considers the correlation of complementary genes and conditions. This eliminates the need for a priori determination of the bicluster size. We employ both greedy search and the genetic algorithm in optimization, incorporating resampling for more robust discovery. When applied to both real and simulation datasets, our results show that CLiP is superior to existing methods. In analyzing RNA-seq fly and worm time-course data from modENCODE, we uncover a set of similarly expressed genes suggesting maternal dependence. Supplementary Material is available online (at www.liebertonline.com/cmb).
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Registered trials
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