Interpolation based consensus clustering for gene expression time series

Background Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizi...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 16; no. 1; p. 117
Main Authors:	Chiu, Tai-Yu, Hsu, Ting-Chieh, Yen, Chia-Cheng, Wang, Jia-Shung
Format:	Journal Article
Language:	English
Published:	London BioMed Central 16.04.2015 BioMed Central Ltd
Subjects:	Algorithms Analysis Bioinformatics Biomedical and Life Sciences Cell Cycle - physiology Cluster Analysis Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer Graphics Consensus Sequence Galactose - metabolism Gene expression Gene Expression Profiling - methods Gene Expression Regulation, Fungal Information management Life Sciences Microarrays Oligonucleotide Array Sequence Analysis - methods Research Article Saccharomyces cerevisiae - genetics Saccharomyces cerevisiae Proteins - genetics Spores, Fungal - physiology Time Factors Transcriptome analysis Interpolation Microarray data analyses Gene expression Consensus clustering Affinity propagation
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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