Neural network based integration of assays to assess pathogenic potential.

ABSTRACT: Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, however, are generated with a specific objective and which makes integration challenging. Here, we developed a deep learning-based approach called the neural network embedding model (NNEM) that integrates data from conventional assays designed to classify species with new assays that interrogate hallmarks of pathogenicity for biothreat assessment. We used a dataset of metabolic characteristics from a de-identified set of known bacterial strains that the Special Bacteriology Reference Laboratory (SBRL) of the Centers for Disease Control and Preve

SUBMITTER: Eslami M 

PROVIDER: S-EPMC10102301 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature