Optimising parallel R correlation matrix calculations on gene expression data using MapReduce

Background High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot...

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Veröffentlicht in:	BMC bioinformatics Jg. 15; H. 1; S. 351
Hauptverfasser:	Wang, Shicai, Pandis, Ioannis, Johnson, David, Emam, Ibrahim, Guitton, Florian, Oehmichen, Axel, Guo, Yike
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 05.11.2014 BioMed Central Ltd Springer Nature B.V
Schlagworte:	Algorithms Analysis Benchmarks Big data Bioinformatics Biomedical and Life Sciences Cluster Analysis Computational Biology/Bioinformatics Computer Appl. in Life Sciences Correlation Gene expression Gene Expression Profiling - methods High-Throughput Nucleotide Sequencing Humans Information management Life Sciences Mathematical analysis Microarrays Optimization Performance evaluation Research Article Snowfall Software Transcriptome analysis Vanilla MapReduce Framework Data Block Data Preparation Hierarchical Cluster Algorithm Vanilla
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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