Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

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.

Grantor dc:publisher

Edith Cowan University, Research Online, Perth, Western Australia

Year dc:date

2020

Author dc:creator

• Mendez, Kevin M.

• Metabolomics
• Artificial Neural Networks
• Machine Learning
• Statistics
• Data Science
• Bioinformatics
• Biology
• Chemistry

Repository record dc:identifier

https://ro.ecu.edu.au/theses/2296

OAI identifier oai:identifier

oai:ro.ecu.edu.au:theses-3298