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:1.7954
- @InProceedings{Orlenko:2018:PSB,
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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",
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title = "Considerations for automated machine learning in
clinical metabolic profiling: Altered homocysteine
plasma concentration associated with metformin
exposure",
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booktitle = "Pacific Symposium on Biocomputing",
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year = "2018",
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editor = "Russ B. Altman and A. Keith Dunker and
Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and
Teri E. Klein",
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pages = "460--471",
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address = "Hawaii, USA",
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month = "3-7 " # jan,
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organisation = "Institute for Computational Biology",
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publisher = "World Scientific",
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keywords = "genetic algorithms, genetic programming, TPOT, AutoML,
Clinical metabolic profiling, Automated machine
learning, Confounding, Metabolomics,
Pharmacometabolomics, Metformin, Homocysteine, Biobank,
Precision medicine",
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isbn13 = "978-981-3235-53-3",
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URL = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882490/",
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URL = "https://pubmed.ncbi.nlm.nih.gov/29218905/",
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URL = "https://psb.stanford.edu/psb-online/proceedings/psb18/orlenko.pdf",
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size = "12 pages",
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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.",
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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