Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data

@article{Skarysz2018ConvolutionalNN,
  title={Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data},
  author={Angelika Skarysz and Yaser Alkhalifah and Kareen Darnley and Michael Eddleston and Yang Hu and Duncan B. McLaren and William Henry Nailon and Dahlia Salman and Martin D. Sykora and C. L. Paul Thomas and Andrea Soltoggio},
  journal={2018 International Joint Conference on Neural Networks (IJCNN)},
  year={2018},
  pages={1-8}
}
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… 

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