Accurate noise-robust classification of Bacillus species from MALDI-TOF MS spectra using a denoising autoencoder

In this study, we used a denoising autoencoder (DAE) to improve bacteria classification accuracy under noisy MS spectra conditions. We employed a denoising autoencoder approach to convert noisy MS spectra into latent variables representing molecular patterns in the original MS data, and the Random Forest method to classify bacterial strains by latent variables. Comparison of the DAE-RF with the CM method using the artificially noisy test samples showed that DAE-RF offers higher noise robustness. Hence, the DAE-RF method could be utilized for noise-robust, fast, and neat classification of Bacillus species according to MALDI-TOF MS data.PMID:37978847 | DOI:10.1515/jib-2023-0017

Source: Journal of integrative bioinformatics - November 18, 2023 Category: Bioinformatics Authors: Yulia E Uvarova Pavel S Demenkov Irina N Kuzmicheva Artur S Venzel Elena L Mischenko Timofey V Ivanisenko Vadim M Efimov Svetlana V Bannikova Asya R Vasilieva Vladimir A Ivanisenko Sergey E Peltek Source Type: research