PathEngine II: A Platform To Automate the Integration of Data To Predict Pathogenic Potential

Netrias, the Texas A&M Health Science Center (TAMHSC) and the Texas A&M Engineering Experiment Station (TEES), and expert consultants will expand and extend the capabilities of PathEngine, an advanced computational platform that ingests and integrates a corpus of bacterial phenotype measurements suitable for the training of a pathogenicity machine learning algorithm. We will enhance the data integration technology and advanced machine learning techniques of PathEngine to rapidly combine and prioritize relevant host and microorganism phenotypic factors to create an accurate, robust, and comprehensive model of pathogenicity for users with little to no computational experience. We will train the platform on 50 additional strains, adding to the 20 strains that were used in Phase I. We will automatically identify and ingest subsets of genomic data from PATRIC, such as virulence factors, and automate the execution of algorithms that are capable of robustly identifying the pathogenic potential of bacteria at >90% accuracy. State-of-the-art methods currently only achieve this accuracy by predicting self-preservation traits using whole genome sequence data.