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  • Biomarker Analysis: Machine Learning and Analytical Approaches to Enhance Diagnostic Accuracy
    (2023) Zhang, Diana
    Thesis
    Early disease diagnosis can significantly improve patient survival rates as appropriate treatment strategies can be timely administered. A promising approach for disease diagnosis is to analyse chemical biomarkers present in bodily fluids as these molecules can provide insights into human metabolic and physiological processes. Changes in the identity and concentrations of such chemicals can help distinguish healthy from disease states. However, some current methods used to collect, analyse, and identify these chemicals have been challenged by limitations in sampling protocols, the resolving power of instruments, and the ability to interpret advanced data analysis methods. This thesis comprises of five concurrent efforts to enhance diagnostic accuracy by investigating various machine learning and analytical approaches. Firstly, an interpretable machine learning framework for binary disease classification is presented. Using this framework on blood plasma and skin sebum data, the diagnostic performance for Parkinson’s disease and key disease biomarkers are reported. Secondly, a protocol and recommendations for robust skin sebum analysis is described. Following a semi-longitudinal study, the various factors that can impact the collection and detection of volatile organic compounds present in skin sebum is discussed. Thirdly, the clinical utility of high-field asymmetric waveform ion mobility spectrometry (FAIMS) for disease diagnosis is reported. Based on a systematic review and meta-analysis, the diagnostic accuracy and clinical implications of using FAIMS is discussed. Fourthly, the performance of high-resolution FAIMS resulting in enhanced ion separations is reported. Using high-resolution FAIMS, the fundamentals that govern the separation of protonation protein isomers is described. Finally, the use of high-resolution FAIMS to analyse volatile organic compounds present in exhaled breath is demonstrated. Using atmospheric pressure chemical ionisation coupled with high-resolution FAIMS, untargeted breath analysis on individual breath profiles is reported. Overall, by improving analytical and machine learning methods, these findings should increase diagnostic accuracy and enable greater confidence in biomarker analysis.