Author Identifiers

Kevin M. Mendez
ORCID: 0000-0002-8832-2607

Date of Award

2020

Degree Type

Thesis

Degree Name

Master of Science (Chemistry)

School

School of Science

First Advisor

Professor David Broadhurst

Second Advisor

Dr Stacey Reinke

Abstract

Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods.

Available for download on Tuesday, April 13, 2021

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