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Analysis Tools for Detection and Diagnosis of Biological Threats

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: DAMD17-04-C-0095
Agency Tracking Number: C041-113-0104
Amount: $99,731.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CBD04-113
Solicitation Number: 2004.1
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-05-15
Award End Date (Contract End Date): 2004-12-15
Small Business Information
4700 Falls of Neuse, Suite 350
Raleigh, NC 27609
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Hong Dang
 Senior Bioinformatician
 (919) 954-0033
Business Contact
 Reese Howle
Title: President
Phone: (919) 954-0033
Research Institution

DNA microarray technology, in combination with statistical and predictive modeling tools, could be used to evaluate thousands of genes against distinct gene expression patterns induced by chemical/biological agents to provide early identification and speed therapeutic intervention. The overall objective of this proposal is to demonstrate the feasibility of building a data management system for DNA microarray study data with integrated computational analysis tools that can provide identification of chemical and biological threats on the basis of host gene response. Alpha-Gamma proposes to accomplish these objectives by first building and populating a prototype relational database with several sets of DNA microarray data along with pathological and physiological endpoints. Alpha-Gamma will build a web-based user interface to browse and select data for analysis, and integrate established statistical tools (e.g., SAS, S-Plus, Spotfire, Patek) into this user friendly environment. With this integration of statistical tools, Alpha-Gamma will perform normalization of microarray data sets, cluster analysis, and pattern recognition. Alpha-Gamma will also validate the potential of this approach for identifying unknown agents by reserving known samples from the datasets and applying statistical tools to identify best match and confidence level.

* Information listed above is at the time of submission. *

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