SVM classification model of similar bacteria species using negative marker: Based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Jongseo Lee, Yoonsu Shin, Songkuk Kim, Kyoohyoung Rho, Kyu H. Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

MALDI-TOF mass spectrometry has high social and economic value in rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Numerous studies have been conducted to identify microorganisms using MALDI-TOF MS. Markers are characteristics that can be used to uniquely distinguish microorganisms. Microorganisms can be identified by applying markers selected based on the extracted mass information. Previous studies demonstrated that combining mass information extracted by MALDI-TOF MS with machine-learning techniques can improve microorganism classification. Classification of microorganisms is particularly difficult and critical for mycobacteria because various pathogens should be treated with different prescriptions, although they exhibit similar compositions. It is quite challenging to accurately identify mycobacteria using conventional methods because their MALDI-TOF MS patterns are similar to each other. In this study, we propose a support vector machine model for improving the distinction of similar species by learning positive and negative markers separately extracted in each group. We classified species in the Mycobacterium abscessus group and Mycobacterium fortuitum group. Our novel approach applies negative markers to classify similar species and improves the identification of similar species using a combination of positive and negative markers.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-150
Number of pages6
ISBN (Electronic)9781538613245
DOIs
Publication statusPublished - 2017 Jul 1
Event17th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2017 - Herndon, United States
Duration: 2017 Oct 232017 Oct 25

Publication series

NameProceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017
Volume2018-January

Other

Other17th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2017
CountryUnited States
CityHerndon
Period17/10/2317/10/25

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biomedical Engineering
  • Modelling and Simulation
  • Signal Processing
  • Health Informatics

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    Lee, J., Shin, Y., Kim, S., Rho, K., & Park, K. H. (2017). SVM classification model of similar bacteria species using negative marker: Based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. In Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017 (pp. 145-150). (Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE.2017.00-64