title: Clustering of High Throughput Gene Expression Data.
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.
authors.affiliation: Department of Industrial and Systems Engineering, Mississippi State University, P.O. Box 9542, Mississippi State, MS 39762.
authors.forename: Harun
authors.initials: H
authors.lastname: Pirim
authors.forename: Burak
authors.initials: B
authors.lastname: Ekşioğlu
authors.forename: Andy
authors.initials: A
authors.lastname: Perkins
authors.forename: Cetin
authors.initials: C
authors.lastname: Yüceer