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PathEngine: A Platform To Automate the Integration of Data To Predict Pathogenic Potential

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D6319C0029
Agency Tracking Number: D18C-002-0076
Amount: $223,201.00
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): 2019-03-07
Award End Date (Contract End Date): 2020-02-10
Small Business Information
1162 Gateway Drive
Annapolis, MD 21409
United States
DUNS: 079773697
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mohammed Eslami
 (202) 213-0191
Business Contact
 Matt Puglisi
Phone: (410) 533-3817
Research Institution
 Texas A&M University
 Paul de Figueiredo Paul de Figueiredo
Division of Research, 301 Old Main Drive 1260 TAMU
College Station, TX 77843
United States

 (979) 436-0699
 Nonprofit College or University

New pathogens, both naturally occurring and adversary-engineered, are increasingly likely to emerge and represent a significant and growing risk to global health and security. These new threats often have limited genetic similarity to prior known pathogens and cannot be identified through standard genetic tests. The application of machine learning algorithms to phenotypic tests to predict pathogenic potential will face challenges in the integration of heterogeneous data sources, and the application of machine learning algorithms to sparse, inconsistent datasets. We propose to build an advanced computational platform called PathEngine that will rapidly ingest and integrate measurements of phenotypic tests from conventional microtiter plate assays, as well as single-cell resolution microfluidics systems. It will use a tailored semi-supervised learning algorithm to predict the pathogenic potential of bacterial strains from limited, sparse, inconsistent training datasets. PathEngine will ingest, integrate, and analyze phenotypic tests of three different categories (harming a host, niche finding, and self-preservation) and be capable of identifying the pathogenic potential of bacteria at >90% accuracy.

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

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