In this section
@article{Tang:2019:10.1371/journal.pone.0218951,
author = {Tang, W and Ranganathan, N and Shahrezaei, V and Larrouy-Maumus, G},
doi = {10.1371/journal.pone.0218951},
journal = {PLoS ONE},
pages = {1--16},
title = {MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.},
url = {http://dx.doi.org/10.1371/journal.pone.0218951},
volume = {14},
year = {2019}
}
TY - JOUR
AB - Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA. Then we concentrate on applying supervised learning to extract and verify the important features. A new workflow is proposed that allows for extracting a fixed set of reference peaks so that any new data can be aligned to it and hence consistent feature matrices can be obtained. Also note that by doing so we are able to examine the robustness of the important features that have been found. We also show that appropriate size of the benchmark data, appropriate alignment of the testing data and use of an optimal set of features via feature selection results in prediction accuracy over 90%. In summary, as proof-of-principle, our integrated experimental and bioinformatics study suggests a novel intact cell MALDI-TOF to be of great promise for fast and reliable detection of MRSA strains.
AU - Tang,W
AU - Ranganathan,N
AU - Shahrezaei,V
AU - Larrouy-Maumus,G
DO - 10.1371/journal.pone.0218951
EP - 16
PY - 2019///
SN - 1932-6203
SP - 1
TI - MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.
T2 - PLoS ONE
UR - http://dx.doi.org/10.1371/journal.pone.0218951
UR - https://www.ncbi.nlm.nih.gov/pubmed/31247021
UR - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218951
UR - http://hdl.handle.net/10044/1/71608
VL - 14
ER -
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