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Pathogen Classification Tool (PACT)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D0420C0019
Agency Tracking Number: D2-2411
Amount: $499,971.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: ST18C-002
Solicitation Number: 18.C
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-02-04
Award End Date (Contract End Date): 2021-02-10
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
 Sowmya Ramachandran
 Principal Investigator
 (650) 931-2700
 sowmya@stottlerhenke.com
Business Contact
 Nate Henke
Phone: (650) 931-2700
Email: nhenke@stottlerhenke.com
Research Institution
 President and Fellows of Harvard College on behalf of Harvard Medical School
 Pernille P. Konow Pernille P. Konow
 
25 Shattuck St., Suite 509
Boston, MA 02115
United States

 (617) 432-1596
 Nonprofit college or university
Abstract

Stottler Henke proposes PACT to address the threat posed by unknown/novel bacteria. Stottler Henke’s solution leverages AI/ML technologies to assess the pathogenic potential of unknown/novel bacteria for DARPA’s Biological Technologies Office. Threat assessment is inferred from phenotype as characterized by a series of assays developed by Harvard University as part of DARPA’s Friend or Foe program. These assays are designed specifically to target various aspects of a bacteria’s phenotype that are heavily correlated with pathogenicity. Examples of the phenotypic features captured in the data include: cellular growth, viability in the presence of different media, niche finding, immune avoidance, & cytotoxicity. We expect, and have observed it to be the case in previous work, that our model will be able to learn a better decision boundary from this carefully curated feature set. The PACT system features a novel semi-supervised neural architecture that is capable of learning from both labeled and unlabeled data. This concept of using an unsupervised task to improve neural network performance has driven an entire thread of neural network research leading to state-of-the-art performance on various tasks. The ultimate goal for this technology is to improve national security by furthering the state-of-the-art in biosurveillance/biodefense technology.

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

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