Caroline WEIS

PhD candidate @ ETH Zurich

Kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination.

The majority of MALDI-TOF MS based machine learning work has employed classical machine learning algorithms for analysis. The development of machine learning algorithms specifically tailored to MALDI-TOF MS based phenotype prediction is still in its infancy. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment.

This talk presents a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. PIKE, the Peak Information Kernel, is a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian Process classifier, provides well-calibrated uncertainty estimates about predictions. The utility of this approach is demonstrated by predicting antibiotic resistance of three clinically highly-relevant bacterial species. This method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data.

References:

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Weis et al., Bioinformatics 2020

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Weis et al., Clinical Microbiology and Infection, March 2020