Encapsulating Generalized Principal Components and Support Vector Machines in a Nonlinear Statistical Pattern Recognition Toxicology Tool

Award Information
Agency:
Department of Defense
Branch
Office of the Secretary of Defense
Amount:
$100,000.00
Award Year:
2003
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-04-M-6421
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
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
094841665
Principal Investigator:
Gary Jahns
Principal Engineer
(858) 812-2994
dklamer@alphatech.com
Business Contact:
John Barry
Contracts Manager
(781) 273-3388
jbarry@alphatech.com
Research Institution:
n/a
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.

* information listed above is at the time of submission.

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