Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Orlenko:2018:PSB,

• author = "Alena Orlenko and Jason H. Moore and Patryk Orzechowski and Randal S. Olson and Junmei Cairns and Pedro J. Caraballo and Richard M. Weinshilboum and Liewei Wang and Matthew K. Breitenstein",
• title = "Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure",
• booktitle = "Pacific Symposium on Biocomputing",
• year = "2018",
• editor = "Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and Teri E. Klein",
• pages = "460--471",
• address = "Hawaii, USA",
• month = "3-7 " # jan,
• organisation = "Institute for Computational Biology",
• publisher = "World Scientific",
• keywords = "genetic algorithms, genetic programming, TPOT, AutoML, Clinical metabolic profiling, Automated machine learning, Confounding, Metabolomics, Pharmacometabolomics, Metformin, Homocysteine, Biobank, Precision medicine",
• isbn13 = "978-981-3235-53-3",
• URL = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882490/",
• URL = "https://pubmed.ncbi.nlm.nih.gov/29218905/",
• URL = "https://psb.stanford.edu/psb-online/proceedings/psb18/orlenko.pdf",
• size = "12 pages",
• abstract = "With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified the suggested association. Increased homocysteine is thought to be associated with vitamin B12 deficiency: evaluation for potential clinical relevance is suggested. While considerations for clinical metabolic profiling are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.",
• notes = "Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA https://psb.stanford.edu/previous/psb18/",

}

Genetic Programming entries for Alena Orlenko Jason H Moore Patryk Orzechowski Randal S Olson Junmei Cairns Pedro J Caraballo Richard M Weinshilboum Liewei Wang Matthew K Breitenstein

Citations