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. Kurt C. ShowmakerAssistant Research Professor
FACULTY
email(662) 325-2455
Pace 120
Biography:
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Clustering of high throughput gene expression data
IGBB Authors:
Andy D. Perkins, Cetin YuceerPUBLICATION YEAR:
2012IMPACT FACTOR:
1.718TIMES CITED (as of August 10, 2017):
28CITATION PERCENTILE:
95Pirim 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.
