Phenotype-Based Threat Assessment
Abstract
Assessing the threat posed by bacterial samples is fundamentally important to safeguarding human health. Whole-genome sequence analysis of bacteria provides a route to achieving this goal. However, this approach is fundamentally constrained by the scope, the diversity, and our understanding of the bacterial genome sequences that are available for devising threat assessment schemes. For example, genome-based strategies offer limited utility for assessing the threat associated with pathogens that exploit novel virulence mechanisms or are recently emergent. To address these limitations, we developed PathEngine, a machine learning strategy that features the use of phenotypic hallmarks of pathogenesis to assess pathogenic threat. PathEngine successfully classified potential pathogenic threats with high accuracy and thereby establishes a phenotype-based, sequence-independent pipeline for threat assessment.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2022
- Source ID
- 10.1073/pnas.2112886119
Entities
People
- Alexandria-jade Roberts
- Alice R. Wattam
- Allan Dickerman
- Angelina Volkova
- Arum Han
- Clark Cucinell
- Dongmei Zhang
- Erin Van Schaik
- Holly Paterson
- James E Samuel
- Jarred Kendziorski
- Jason Maples
- Jennifer Dootz
- Jing Yang
- Kasra Faghihi
- Mark Weston
- Mayukh Das
- Mohammed Eslami
- Osahon Obanor
- Paul de Figueiredo
- Qing-Ming Qin
- Robbie K. Moore
- Robert C. Alaniz
- S. Coburn
- Shaorong Chen
- Stephanie Servetas
- Yi-pei Chen
Organizations
- National Institute of Standards and Technology
- University of Virginia