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Encapsulating Generalized Principal Components and Support Vector Machines in a…

Award Information

Agency:
Department of Defense 		Branch:
Office of the Secretary of Defense 		Award ID:
66871 		Program Year/Program:
2003 / SBIR
Agency Tracking Number:
O032-3084 		Solicitation Year:
N/A 		Solicitation Topic Code:
N/A 		Solicitation Number:
N/A
Small Business Information
ALPHATECH, INC.
6 New England Executive Park Burlington, MA 01803
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2003
Title: Encapsulating Generalized Principal Components and Support Vector Machines in a Nonlinear Statistical Pattern Recognition Toxicology Tool
Agency / Branch: DOD / OSD
Contract: FA8650-04-M-6421
Award Amount: $100,000.00
 

Abstract:

Identifying toxic-substance exposure at low, subtoxic concentrations requires interpretation of complex, time-related changes in gene, protein, and metabolite expression patterns. We propose a statistical pattern recognition software tool based onflexible and adaptable modules. In Phase I, we will produce the tool framework and Principal Components Analysis (PCA) and Support Vector Machine (SVM) modules. The tool framework preprocesses genomic, proteomic, and metabonomic data sets from clinicalsources. PCA linearly transforms each type of data to an orthogonal space of significantly reduced dimension in which expression of like toxins are clustered. Through Statistical Learning Theory, the SVM adapts and estimates a nonlinear mapping functionfrom the expression-data input space to a decision feature space using data for which ground truth has been independently established. A similarity kernel in feature space induces a metric on the input space by selecting key feature components andproduces a nonlinear decision boundary. Selection of the kernel can range from a simple distance metric to neural networks (multi-layer perceptrons, radial/elliptical basis function networks, etc) to fuzzy membership functions. By changing the kernel,the performance of the SVM decision boundaries can be optimized over a range of kernel similarity metrics, feature mappings, and feature selection. Other modules may be added in the future to implement a wider suite of solutions in a user-friendlyanalysis environment.

Principal Investigator:

Gary L. Jahns
Principal Engineer
8588122994
dklamer@alphatech.com

Business Contact:

John J. Barry
Contracts Manager
7812733388
jbarry@alphatech.com
Small Business Information at Submission:

ALPHATECH, INC.
6 New England Executive Park Burlington, MA 01803

EIN/Tax ID: 042654515
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No