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Machine learning techniques for the early detection of cancer using volatile organic compounds

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Neal, Matthew (2019) Machine learning techniques for the early detection of cancer using volatile organic compounds. PhD thesis, University of Warwick.

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Official URL: http://webcat.warwick.ac.uk/record=b3452905~S15

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Item Type: Thesis or Dissertation (PhD)
Subjects: Q Science > QD Chemistry
Q Science > QP Physiology
R Medicine > RC Internal medicine
Library of Congress Subject Headings (LCSH): Cancer -- Early detection, Volatile organic compounds, Ion mobility spectroscopy
Official Date: January 2019
Dates:
DateEvent
January 2019Published
Institution: University of Warwick
Theses Department: Department of Statistics
Thesis Type: PhD
Publication Status: Unpublished
Supervisor(s)/Advisor: Savage, Richard S.
Description:

Early cancer detection can change lives. If cancer can be detected early in its development, while a patient is still asymptomatic, it is easier and less expensive to treat. Volatile organic compounds, as measured by field asymmetric ion mobility spectrometry (FAIMS), are a novel class of biomarkers which have shown promise as a low-cost early screening test for a range of cancers. However, FAIMS data is high-dimensional, difficult to interpret, and can be subject to a range of subtle data quality problems. Additionally, in the current literature many researchers use linear methods for analysing FAIMS data. We believe improved results could be achieved by applying modern machine learning techniques to the problem.

In this thesis, we investigate using modern machine learning techniques and best practices for FAIMS analysis, and develop corresponding software. We found that FAIMS has a moderate ability to detect cancer in an at-risk population, and that FAIMS could be used for the pre-symptomatic detection of other diseases with excellent results, achieving an AUC of 0.91 for the pre-symptomatic detection of anastomotic leakage after surgical resection to treat cancer.

We present a novel Bayesian dimensionality reduction technique, the structured Gaussian process latent variable model (SGPLVM), which extends GPLVM to exploit structured correlations between variables, as are seen in FAIMS data. We also present a stochastic optimization algorithm and a number of extensions based on stochastic gradient descent variants. We explore the properties of SGPLVM, which we found to outperform GPLVM at recovering a latent representation of data which meet the model assumptions. We also demonstrate SGPLVM’s robustness to partially observed data.

Finally, we present software packages for GPLVM, SGPLVM, and GP regression with a novel method of specifying and automatically selecting compound kernel functions.

Extent: xv, 210 leaves: illustration, charts, maps
Language: eng

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