The IGBB is a member of the NIH-funded Mississippi IDeA Network of Biomedical Research Excellence (Mississippi INBRE or MS-INBRE), a program designed to build biomedical infrastructure throughout the state. Through MS-INBRE, Mississippi students and faculty at Mississippi's undergraduate institutions are (1) trained in biomedical research techniques, (2) given the opportunity to work with top researchers at Mississippi's major research universities, (3) afforded access to state-of-the-art bioscience equipment, and (4) provided with assistance in preparing grant proposals. The IGBB serves as the MS-INBRE proteomics/computational biology core. For more information about MS-INBRE, click here.
Dr. Chuan-Yu Hsu (Sue)Senior Research Associate
GENOMICS LEAD
email(662) 325-9511
Pace 121
Clustering of high throughput gene expression data
IGBB Authors:
Andy D. Perkins, Cetin YuceerPUBLICATION YEAR:
2012IMPACT FACTOR:
3.372CITATION COUNT:
72Pirim 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.
