Clustering of high throughput gene expression data |
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Authors: | Harun Pirim,Burak Ekşioğlu,Andy D. Perkins,Ç etin Yü ceer |
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Affiliation: | 1. Department of Industrial and Systems Engineering, Mississippi State University, P.O. Box 9542, Mississippi State, MS 39762, United States;2. Department of Computer Science and Engineering, Mississippi State University, United States;3. Department of Forestry, Mississippi State University, United States |
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Abstract: | High 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. |
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Keywords: | Clustering Bioinformatics Gene expression data High throughput data Microarrays |
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