Clinical antimicrobial resistance prediction based on MALDI-TOF MS

What you will discover: a dataset created for clinical antimicrobial resistance prediction based on MALDI-TOF MS

You are: a researcher working in the application of phenotype prediction algorithms,
especially on vector represented data, collected across from different domains

Speaker: Caroline Weis, an AI/ML Engineer at GSK.ai

Click to access specific sections of the talk:
00:32 Purposes of the DRIAMS dataset
01:16 The threat and treatment of antimicrobial resistance
04:31 The database of resistance information on antimicrobials and MALDI-TOF mass spectra
06:26 Antimicrobial resistance prediction based on DRIAMS
09:16 Application for DRIAMS

Reference papers and resource
Weis C et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat Med 2022;28(1):164-174 https://rdcu.be/cNkKU

Complete DRIAMS database publicly available at: Weis, Caroline et al. (2022), DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra, Dryad, Dataset, https://doi.org/10.5061/dryad.bzkh1899q.

All R and Python scripts can be found in https://github.com/BorgwardtLab/maldi... under a BSD 3-Clause License.

Weis C et al. Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. OUP Bioinformatics, accepted at ISMB 2020. https://doi.org/10.1093/bioinformatic...

Weis C et al. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clinical Microbiology and Infection, 2020. https://doi.org/10.1016/j.cmi.2020.03...

Weis C et al. Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification. bioRxiv, 2022. https://doi.org/10.1101/2022.04.13.48...

Any question about this talk? Contact Caroline at caroline.weis[at]bsse.ethz.ch

More about the speaker:
Caroline Weis obtained her PhD in machine learning for computational biology from ETH Zurich in the SIB Group of Karsten Borgwardt (https://bsse.ethz.ch/mlcb), where she focused on predicting antimicrobial resistance from early available microbial mass spectra. Throughout her career, she has focused on improving the biological understanding of diseases and patient outcomes in healthcare by leveraging machine learning and bioinformatic models. Currently she is working as an AI/ML Engineer for Biomedical AI at GSK.ai, where she develops machine learning tools for cancer immunotherapy and personalized medicine focusing on interpretability and causal models.


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