RT Journal Article SR Electronic T1 Extended-Spectrum Beta-Lactamase and Carbapenemase-Producing prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.10.04.463058 DO 10.1101/2021.10.04.463058 A1 A. Guerrero-López A1 C. Sevilla-Salcedo A1 A. Candela A1 M. Hernández-García A1 E. Cercenado A1 P. M. Olmos A1 R. Cantón A1 P. Muñoz A1 V. Gómez-Verdejo A1 R. del Campo A1 B. Rodríguez-Sánchez YR 2022 UL http://biorxiv.org/content/early/2022/02/02/2021.10.04.463058.1.abstract AB Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification. Currently, machine learning techniques are used to predict Antibiotic Resistance (AR) based on MALDI-TOF data. However, current solutions need costly preprocessing steps, their reproducibility is difficult due to hyperparameter cross-validation, they do not provide interpretable results, and they do not take into account the epidemiological difference inherent to data coming from different laboratories. In this paper, we validate a multi-view heterogeneous Bayesian model (SSHIBA) for AR mechanism prediction based on MALDI-TOF MS. This novel approach allows exploiting local epidemiology differences between data sources, gets rid of preprocessing steps, is easily reproducible because hyperparameters are optimized by Bayesian inference, and provides interpretable results. To validate this model and its advantages, we present two domains of Klebsiella pneumoniae isolates: 282 samples of Hospital General Universitario Gregorio Marañón (GM) domain and 120 samples for Hospital Universitario Ramón y Cajal (RyC) domain that discriminates between Wild Type (WT), Extended-Spectrum Beta-Lactamases (ESBL)-producers and ESBL + Carbapenemases (ESBL+CP)-producers. Experimental results prove that SSHIBA outperforms state-of-the-art (SOTA) algorithms by exploiting the multi-view approach that allows it to distinguish between data domains, avoiding local epidemiological problems. Moreover, it shows that there is no need to preprocess MALDI-TOF data. Its implementation in microbiological laboratories could improve the detection of multi-drug resistant isolates, optimizing the therapeutic decision and reducing the time to obtain results of the resistance mechanism. The proposed model implementation, specifically adapted to AR prediction, and data collections are publicly available on GitHub at: github.com/alexjorguer/RMPredictionCompeting Interest StatementThe authors have declared no competing interest.