PMID- 36674514
OWN - NLM
STAT- MEDLINE
DCOM- 20230124
LR  - 20230202
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 24
IP  - 2
DP  - 2023 Jan 5
TI  - Towards Accurate Identification of Antibiotic-Resistant Pathogens through the 
      Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.
LID - 10.3390/ijms24020998 [doi]
LID - 998
AB  - Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass 
      spectrometry (MS) has been used to identify microorganisms and predict antibiotic 
      resistance. The preprocessing method for the MS spectrum is key to extracting 
      critical information from complicated MS spectral data. Different preprocessing 
      methods yield different data, and the optimal approach is unclear. In this study, 
      we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, 
      MALDIquant, and continuous wavelet transform-based methods--to detect peaks and 
      build machine learning classifiers, including logistic regressions, naive Bayes 
      classifiers, random forests, and a support vector machine. The aim was to 
      identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter 
      nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on 
      MALDI-TOF MS spectra collected from two branches of a referral tertiary medical 
      center. The ensemble method was compared with the individual methods. Random 
      forest models built with the data preprocessed by the ensemble method 
      outperformed individual preprocessing methods and achieved the highest accuracy, 
      with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. 
      faecium), and 70.12% (GBS) on independent testing datasets. Through feature 
      selection, important peaks related to antibiotic resistance could be detected 
      from integrated information. The prediction model can provide an opinion for 
      clinicians. The discriminative peaks enabling better prediction performance can 
      provide a reference for further investigation of the resistance mechanism.
FAU - Chung, Chia-Ru
AU  - Chung CR
AUID- ORCID: 0000-0002-4548-7620
AD  - Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese 
      University of Hong Kong, Shenzhen 518172, China.
AD  - School of Life Sciences, University of Science and Technology of China, Hefei 
      230026, China.
FAU - Wang, Hsin-Yao
AU  - Wang HY
AUID- ORCID: 0000-0001-5581-6793
AD  - Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, 
      Taoyuan 333423, Taiwan.
AD  - Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan 333323, 
      Taiwan.
FAU - Chou, Po-Han
AU  - Chou PH
AD  - Department of Computer Science and Information Engineering, National Central 
      University, Taoyuan 320317, Taiwan.
FAU - Wu, Li-Ching
AU  - Wu LC
AUID- ORCID: 0000-0002-6323-4555
AD  - Department of Biomedical Sciences and Engineering, National Central University, 
      Taoyuan 320317, Taiwan.
FAU - Lu, Jang-Jih
AU  - Lu JJ
AD  - Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, 
      Taoyuan 333423, Taiwan.
AD  - Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 
      333323, Taiwan.
AD  - College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan.
AD  - Department of Medical Biotechnology and Laboratory Science, Chang Gung 
      University, Taoyuan 333323, Taiwan.
FAU - Horng, Jorng-Tzong
AU  - Horng JT
AD  - Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese 
      University of Hong Kong, Shenzhen 518172, China.
AD  - Department of Bioinformatics and Medical Engineering, Asia University, Taichung 
      41354, Taiwan.
FAU - Lee, Tzong-Yi
AU  - Lee TY
AUID- ORCID: 0000-0001-8475-7868
AD  - Warshel Institute for Computational Biology, School of Life and Health Sciences, 
      The Chinese University of Hong Kong, Shenzhen 518172, China.
AD  - Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung 
      University, Hsinchu 300093, Taiwan.
LA  - eng
GR  - CMRPG3L0402/Linkou Chang Gung Memorial Hospital/
GR  - CMRPG3L0432/Linkou Chang Gung Memorial Hospital/
GR  - CMRPG3M0851/Linkou Chang Gung Memorial Hospital/
GR  - CMRPG3L1011/Linkou Chang Gung Memorial Hospital/
GR  - 111-2320-B-182A-002-MY2/Ministry of Science and Technology, Taiwan/
PT  - Journal Article
DEP - 20230105
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
RN  - 0 (Anti-Bacterial Agents)
SB  - IM
MH  - Humans
MH  - Anti-Bacterial Agents/pharmacology
MH  - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
MH  - Bayes Theorem
MH  - *Acinetobacter baumannii/chemistry
MH  - *Acinetobacter Infections
PMC - PMC9865071
OTO - NOTNLM
OT  - MALDI-TOF MS
OT  - antibiotic resistance
OT  - machine learning
COIS- The authors have no affiliations with or involvement in any organization or 
      entity with any financial interest or non-financial interest in the subject 
      matter or materials discussed in this manuscript.
EDAT- 2023/01/22 06:00
MHDA- 2023/01/25 06:00
PMCR- 2023/01/05
CRDT- 2023/01/21 01:24
PHST- 2022/11/15 00:00 [received]
PHST- 2022/12/24 00:00 [revised]
PHST- 2022/12/27 00:00 [accepted]
PHST- 2023/01/21 01:24 [entrez]
PHST- 2023/01/22 06:00 [pubmed]
PHST- 2023/01/25 06:00 [medline]
PHST- 2023/01/05 00:00 [pmc-release]
AID - ijms24020998 [pii]
AID - ijms-24-00998 [pii]
AID - 10.3390/ijms24020998 [doi]
PST - epublish
SO  - Int J Mol Sci. 2023 Jan 5;24(2):998. doi: 10.3390/ijms24020998.