Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra
| Autor: | Adrian Egli, Aline Cuénod, Bastian Rieck, Caroline Weis, Max Horn, Karsten M. Borgwardt |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science Similarity measure Mass spectrometry Biochemistry 03 medical and health sciences 0302 clinical medicine Humans Bioinformatics of Microbes and Microbiomes Molecular Biology Bacteria business.industry Single parameter Pattern recognition Drug Resistance Microbial Computer Science Applications Anti-Bacterial Agents Computational Mathematics Statistical classification 030104 developmental biology Phenotype Computational Theory and Mathematics 030220 oncology & carcinogenesis Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Mass spectrum Artificial intelligence business Classifier (UML) |
| Zdroj: | Bioinformatics Bioinformatics, 36 (1) |
| ISSN: | 1367-4803 1460-2059 |
| DOI: | 10.3929/ethz-b-000427816 |
| Popis: | Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our 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. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care. Bioinformatics, 36 (1) ISSN:1367-4803 ISSN:1460-2059 |
| Databáze: | OpenAIRE |
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