Open AccessPhenotype-Based Threat Assessment
Author(s) -
Jing Yang,
Mohammed Eslami,
Yi-Pei Chen,
Mayukh Das,
Dongmei Zhang,
Shaorong Chen,
Alexandria-Jade Roberts,
Mark Weston,
Angelina Volkova,
Kasra Faghihi,
Robbie K Moore,
Robert C. Alaniz,
Allan Dickerman,
Allan Dickerman,
Clark A Cucinell,
Jarred Kendziorski,
S. Coburn,
Holly Paterson,
Osahon Obanor,
Jason Maples,
Stephanie L. Servetas,
Jennifer N. Dootz,
QingMing Qin,
James E. Samuel,
Arum Han,
Erin J. van Schaik,
Paul de Figueiredo
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2112886119
Subject(s) - virulence , phenotype , biology , identification (biology) , pathogen , computational biology , human pathogen , genetics , genome , gene , botany
Significance 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.