Koninklijke Philips Corporation N.V.

Karthikeyan Murugesan

Nevenka Dimitrova

Henry Lin

Pramod Mayigowda

Methods and systems for predicting the susceptibility of bacterial pathogens to antibiotics using genomic data sets. Various embodiments described herein receive a genomic dataset and a set of labels and run principal variance component analysis thereon to determine the effect sizes of the labels. One or more labels are then selected based on their effect sizes and used in a machine learning model to make predictions on future datasets.

Analytic prediction of antibiotic susceptibility

Methods and systems for identifying causal genetic mechanisms of antibiotic resistance in pathogens. In accordance with at least one embodiment, the system includes a gene resistance module to identify genes present in an antibiotic resistant pathogen, a single nucleotide polymorphism module to identify mutations present in an antibiotic resistant pathogen, and an antibiotic resistance module configured to output the causation of antibiotic resistance based on the identified genes and mutations.

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Cambridge, MA, United States of America

Karthikeyan Murugesan