{"id":{"repo_id":"edithcowan","oai_identifier":"oai:ro.ecu.edu.au:theses-3298"},"canonical_url":"https://search.dev.ndltd.org/etd/edithcowan/oai:ro.ecu.edu.au:theses-3298","repository":{"repo_id":"edithcowan","name":"Edith Cowan University","base_url":"https://ro.ecu.edu.au/do/oai/"},"display":{"title":"Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Mendez, Kevin M."],"institution":"Edith Cowan University, Research Online, Perth, Western Australia","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-27T19:21:32Z","subjects":["Metabolomics","Artificial Neural Networks","Machine Learning","Statistics","Data Science","Bioinformatics","Biology","Chemistry"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://ro.ecu.edu.au/theses/2296","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mendez, Kevin M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-01-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Edith Cowan University, Research Online, Perth, Western Australia"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Metabolomics","Artificial Neural Networks","Machine Learning","Statistics","Data Science","Bioinformatics","Biology","Chemistry"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://ro.ecu.edu.au/theses/2296","https://ro.ecu.edu.au/context/theses/article/3298/viewcontent/MScFINALThesis_KMM_for_RO.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:source","label":"Dc Source","values":["Theses: Doctorates and Masters"]},{"key":"dc:title","label":"Title","values":["Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data"]}]}],"canonical_facts":{"dc:creator":["Mendez, Kevin M."],"dc:date":["2020-01-01T08:00:00Z"],"dc:description":["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."],"dc:format":["application/pdf"],"dc:identifier":["https://ro.ecu.edu.au/theses/2296","https://ro.ecu.edu.au/context/theses/article/3298/viewcontent/MScFINALThesis_KMM_for_RO.pdf"],"dc:publisher":["Edith Cowan University, Research Online, Perth, Western Australia"],"dc:source":["Theses: Doctorates and Masters"],"dc:subject":["Metabolomics","Artificial Neural Networks","Machine Learning","Statistics","Data Science","Bioinformatics","Biology","Chemistry"],"dc:title":["Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data"],"dc:type":["thesis"]},"updated_at":"2026-07-27T19:21:32Z"}