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Clustering of high throughput gene expression data
IGBB Authors:
Andy D. Perkins, Cetin YuceerPUBLICATION YEAR:
2012IMPACT FACTOR:
3.372CITATION COUNT:
102Pirim H, Eksioglu B, Perkins AD, Yuceer C (2012) Clustering of high throughput gene expression data.
Computers & Operations Research 39(12): 3046-3061.
DOI:
10.1016/j.cor.2012.03.008EID:
2-s2.0-84862994964PMID:
DOWNLOAD PDFABSTRACTHigh throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community. -¬ 2012 Elsevier Ltd. All rights reserved.
The IGBB is supported, in part, by the following units:
The IGBB is an HPC² member center.
