You are here

Pathogen Classification Tool (PaCT)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D6319C0030
Agency Tracking Number: D18C-002-0069
Amount: $224,979.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST18C-002
Solicitation Number: 18.C
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-06
Award End Date (Contract End Date): 2020-02-10
Small Business Information
1650 South Amphlett Blvd. Suite 300
San Mateo, CA 94402
United States
DUNS: 608176715
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Richard Stottler
 Principal Investigator
 (650) 931-2700
Business Contact
 Nate Henke
Phone: (650) 931-2700
Research Institution
 The Monack Lab at Stanford University School of Medicine
 Nikki H. Williams Nikki H. Williams
299 Campus Drive, Fairchild Building D331
Stanford, CA 94305
United States

 (650) 736-3228
 Nonprofit College or University

Stottler Henke proposes PaCT, leveraging our related past work in computer vision and machine learning. Drawing from techniques used in ExPATSS, a Phase II SBIR effort slated for transition to the Naval fleet, PaCT will perform bacterial characterization using features derived from the phenotype of the bacteria. PaCT will predict bacterial characteristics such as pathogenicity, antibiotic resistance, and ability to cause death in humans from features identified by our Subject Matter Experts (SME). PaCT will incorporate only understood features, known to correlate with the target variables, into its models. This restriction on the model features will increase the likelihood that PaCT will remain robust to divergent and/or novel inputs. PaCT is designed to be a fast and easy to use support system for research, military, and healthcare personnel. Our classification tool offers in-depth analysis of prediction results to identify the salient features which informed a given classification. PaCT also provides analytical tools that offer the end-user insights derived from high dimensional data. Stottler Henke has a strong track record of moving state-of-the-art artificial intelligence techniques from academia into industry. We anticipate a limited prototype of TRL 4 to demonstrate feasibility at the completion of Phase I.

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

US Flag An Official Website of the United States Government