Author

Patrick James Trainor

University of LouisvilleORCID 0000-0002-3012-9716Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:metabolomics· 3 Research Focus:benchmarking· 2 Organism:human· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Andrew Paul DeFilippisiD· 3 shared Shesh Nath RaiiD· 2 shared Hunter MoseleyiD· 1 shared Joshua M. MitchelliD· 1 shared Roman V. YampolskiyiD· 1 shared Samantha M. CarlisleiD· 1 shared

Activity by Year

2017-2018 · peak 2/yr

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6 top collaborators · 11 mutual co-author links

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3 shared papersBioinformatics

Andrew Paul DeFilippisORCID

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3 papers

Recent·Most cited

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

Patrick James Trainor, Joshua M. Mitchell, Samantha M. Carlisle, Hunter Moseley, Andrew Paul DeFilippis, Shesh Nath Rai

The paper develops a Bayesian graphical model to infer metabolite interactomes using molecular structure information and applies it to coronary artery disease data.

bioRxiv · Aug 7, 2018 · PreprintSystems Biology

Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

Patrick James Trainor, Roman V. Yampolskiy, Andrew Paul DeFilippis

This paper applies a Wisdom of Artificial Crowds (WoAC) strategy to feature selection in untargeted metabolomics for developing a blood-based diagnostic test for thrombotic myocardial infarction, achieving lower misclassification rates than Lasso and Elastic Net.

Journal of Biomedical Informatics · Mar 22, 2018Peer-reviewedBioinformatics

Evaluation Of Classifier Performance For Multiclass Phenotype Discrimination In Untargeted Metabolomics

Patrick James Trainor, Andrew Paul DeFilippis, Shesh Nath Rai

The paper evaluates classifier performance for multiclass phenotype discrimination in untargeted metabolomics using simulated and real data, finding that SVM and Random Forest outperform PLS-DA and Neural Networks in most scenarios.

Metabolites · Jun 21, 2017Peer-reviewedBioinformatics