Date of Award
Master of Science (Chemistry)
School of Science
Professor David Broadhurst
Dr Stacey Reinke
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.
Mendez, K. M. (2020). Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data. https://ro.ecu.edu.au/theses/2296
Available for download on Tuesday, April 13, 2021