The IGBB logo features a stylized "pinwheel" to the left of the letters IGBB in caps in a modified Bank Gothic Pro font.
The six-part "pinwheel" in the IGBB logo is:
- A symbol of lab unity as it shows "parts" coming together to make a "whole."
- A flower or three-leaf clover representing (a) plants, important subjects of our research, (b) life in general, and (c) the life sciences (biology).
- A set of chromosomes being moved towards the center of a cell.
- The Sun - another symbol of life.
- A protein composed of six subunits (e.g., a protein pore).
- Three foxes putting their heads together. The fox is a symbol of cleverness in Western folklore. Since the IGBB is organized into three service groups (Genomics, Proteomics/Metabolomics, and Biocomputing/Computational Biology), the foxes could represent the three disciplines working together.
- A scientist jumping for joy after making an important discovery.
- A windmill, the primary symbol associated with Cervantes' famous character Don Quixote - Like Don Quixote, scientists must be willing to attack 'wicked giants' (e.g., ignorance, racism, sexism, intolerance, use of the term 'science' in the promotion of non-scientific causes), champion worthy causes (e.g., education, intellectual freedom, human rights, environmental responsibility), and remain optimistic in the face of defeat (e.g., most days in the lab). Hopefully, however, the average scientist can accomplish these tasks without becoming delusional (a problem that squashed Quixote's dreams of becoming a plant molecular biologist).
- A DNA double-helix or protein in cross section.
- Antibodies binding to a protein.
- Whatever you want it to be.
Dr. Zenaida V. MagbanuaSenior Research Associate
GENOMICS
email(662) 325-7647
Pace 120
Clustering of high throughput gene expression dataIGBB Authors:
Andy D. Perkins, Cetin YuceerPUBLICATION YEAR:
2012IMPACT FACTOR:
3.372CITATION COUNT:
93Pirim 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.