ArticleScientific reports2017
A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- An integrated photonics engine for unsupervised correlation detection.Science advances · 2022Article
- Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis.Patterns (New York, N.Y.) · 2021Review
- Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.Medical & biological engineering & computing · 2021Article
- Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers.Molecules (Basel, Switzerland) · 2021Article
- EBIC: an open source software for high-dimensional and big data analyses.Bioinformatics (Oxford, England) · 2019Article
- EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.Bioinformatics (Oxford, England) · 2018Article
- Seed Dormancy Involves a Transcriptional Program That Supports Early Plastid Functionality during Imbibition.Plants (Basel, Switzerland) · 2018Article
- ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems.PloS one · 2018Article
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2 authors.
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Abstract
In the analysis of large-scale gene expression data, it is important to identify groups of genes with common expression patterns under certain conditions. Many biclustering algorithms have been developed to address this problem. However, comprehensive discovery of functionally coherent biclusters from large datasets remains a challenging problem. Here we propose a GPU-accelerated biclustering algorithm, based on searching for the largest Condition-dependent Correlation Subgroups (CCS) for each gene in the gene expression dataset. We compared CCS with thirteen widely used biclustering algorithms. CCS consistently outperformed all the thirteen biclustering algorithms on both synthetic and real gene expression datasets. As a correlation-based biclustering method, CCS can also be used to find condition-dependent coexpression network modules. We implemented the CCS algorithm using C and implemented the parallelized CCS algorithm using CUDA C for GPU computing. The source code of CCS is available from https://github.com/abhatta3/Condition-dependent-Correlation-Subgroups-CCS.
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