Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a emerging metabolomics research field that mainly focuses on health- volatile organic compounds (VOCs). Since the amount of these compounds with health status, breathomics holds great promise to deliver non- diagnostic tools. Thus, the main aim of breathomics is to find patterns related to abnormal (for instance inflammatory) metabolic processes the human body. Recently, analytical methods for measuring VOCs in with high resolution and high throughput have been extensively the application of machine learning methods for fingerprinting VOC the breathomics is still in its infancy. Therefore, in this paper, we the current state of the art in data pre-processing and multivariate breathomics data. We start with the detailed pre-processing pipelines breathomics data obtained from gas-chromatography mass spectrometry and ion-mobility spectrometer coupled to multi-capillary columns. The pre-processing is a matrix containing the relative abundances of a set for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most question, 'which VOCs are discriminatory?', remains the same. Answers given by several modern machine learning techniques (multivariate and, therefore, are the focus of this paper. We demonstrate the well the drawbacks of such techniques. We aim to help the community to how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched breathomics.