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Machine Learning and Data Fusion platform for Phenotype-based Pathogen Identification

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D6319C0031
Agency Tracking Number: D18C-002-0039
Amount: $224,981.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST18C-002
Solicitation Number: 18.C
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-13
Award End Date (Contract End Date): 2020-02-14
Small Business Information
200 TURNPIKE ROAD
CHELMSFORD, MA 01824
United States
DUNS: 796010411
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kim Yoojeong
 Principal Investigator
 (978) 856-4172
 ykim@tritonsystems.com
Business Contact
 Collette E Jolliffe
Phone: (978) 250-4200
Email: cjolliffe@tritonsystems.com
Research Institution
 Boston College
 Collette E Jolliffe Collette E Jolliffe
 
140 Commonwealth Ave Higgins Hall, 420A
Chestnut Hill, MA 02467
United States

 (978) 856-4183
 Nonprofit college or university
Abstract

Conventional methods for detecting pathogens, which are based on culturing the microorganism, are time-consuming and laborious. Machine learning provides an alternative path to identify pathogens using supervised learning algorithms. Most current computational tools utilize genomic or protein data to identify bacteria. These methods look for features in the whole genome that correlate to pathogenicity. If whole genome or closely related data is available, models can accurately predict pathogenicity. However, obtaining genetic data is time-consuming process and fails to accurately predict or identify novel species. Genetically engineered bacteria are particularly difficult to identify due to lack of sequence data. In addition, identifying new bacterial pathogens that have emerged from parallel evolution is a challenging task. An alternative approach is using phenotypic data. Phenotypic data, such as niche response, self-preservation, and the ability to harm a host, model how an organism behaves and is not susceptible to same kind of limitations a genetic approach has. Triton Systems, Inc. proposes to develop a machine learning algorithm and data fusion technique to predict pathogenicity based on phenotypic data. If successful, the software will provide an effective threat identification and countermeasure tool.

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

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