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Title: Convolutional neural networks for automated targeted analysis of gas chromatography-mass spectrometry data
Authors: Skarysz, Angelika
Alkhalifah, Yaser
Darnley, Kareen
Eddleston, Michael
Hu, Yang
McLaren, Duncan B.
Nailon, William H
Salman, Dahlia
Sykora, Martin D.
Thomas, C.L. Paul
Soltoggio, Andrea
Issue Date: 2018
Publisher: © IEEE
Citation: SKARYSZ, A. ... et al, 2018. Convolutional neural networks for automated targeted analysis of gas chromatography-mass spectrometry data. IN: 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8-13 July 2018.
Abstract: Through their breath, humans exhale hundreds of volatile organic compounds (VOCs) that can reveal pathologies, including many types of cancer at early stages. Gas chromatography–mass spectrometry (GC-MS) is an analytical method used to separate and detect compounds in the mixture contained in breath samples. The identification of VOCs is based on the recognition of their specific ion patterns in GC-MS data, which requires labour-intensive and time-consuming preprocessing and analysis by domain experts. This paper explores the original idea of applying supervised machine learning, and in particular convolutional neural networks (CNNs), to learn ion patterns directly from raw GC-MS data. The method adapts to machine specific characteristics, and once trained, can quickly analyse breath samples bypassing the time-consuming preprocessing phase. The CNN classification performance is compared to those of shallow neural networks and support vector machines. All considered machine learning tools achieved high accuracy in experiments with clinical data from participants. In particular, the CNN-based approach detected the lowest number of false positives. The results indicate that the proposed method is a promising tool to improve accuracy, specificity, and in particular speed in the detection of VOCs of interest in large-scale data analysis.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This study was partially funded by the EU H2020 TOXI-Triage Project #653409.
Version: Accepted for publication
DOI: 10.1109/IJCNN.2018.8489539
URI: https://dspace.lboro.ac.uk/2134/33052
Publisher Link: https://doi.org/10.1109/IJCNN.2018.8489539
ISBN: 9781509060146
ISSN: 2161-4407
Appears in Collections:Conference Papers and Presentations (Chemistry)
Conference Papers and Presentations (Computer Science)

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