Publisher
Edith Cowan University
Degree Name
Master of Science (Chemistry)
First Supervisor
Professor David Broadhurst
Second Supervisor
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.
Related Publications
Mendez, K.M, Broadhurst, D.I., Reinke, S.N. (2019) The Application of Artificial Neural Networks in Metabolomics: A Historical Perspective. Metabolomics, 15, 142. doi:10.1007/s11306-019-1588-0. Link to article available here.
Mendez, K.M., Pritchard, L., Reinke, S.N., Broadhurst, D.I. (2019) Toward Collaborative Open Data Science in Metabolomics using Jupyter Notebooks and Cloud Computing. Metabolomics, 15, 125. doi:org/10.1007/s11306-019-1588-0. Link to article available here.
Mendez, K.M, Reinke, S.N., Broadhurst, D.I. (2019) A Comparative Evaluation of the Generalised Predictive Ability of Eight Machine Learning Algorithms across Ten Clinical Metabolomics Data Sets for Binary Classification. Metabolomics, 15, 150. doi:10.1007/s11306-019-1612-4 Link to article available here.
Mendez, K.M, Broadhurst, D.I., Reinke, S.N. (2019) Migrating from Partial Least Squares Discriminant Analysis to Artificial Neural Networks: A Comparison of Functionally Equivalent Visualisation and Feature Contribution Tools using Jupyter Notebooks. Metabolomics, 16, 17 doi:10.1007/s11306-020-1640-0 Link to artile available here
Mendez, K.M., Reinke, S.N., Broadhurst, D.I. (2019) The Application of Artificial Neural Networks in Metabolomics. The 15th Annual Conference of the Metabolomics Society, 23rd - 27th June. The Hague, Netherlands.
Recommended Citation
Mendez, K. M.
(2020).
Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data.
Edith Cowan University.
Retrieved from
https://ro.ecu.edu.au/theses/2296
