@article {43012,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	journal = {bioRxiv},
	year = {2023},
	publisher = {bioRxiv},
	doi = {https://doi.org/10.1101/2022.12.19.521154 },
	url = {https://www.biorxiv.org/content/10.1101/2022.12.19.521154v2},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Horner, David and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {43402,
	title = {Characterizing human postprandial metabolic response using multiway data analysis},
	journal = {bioRxiv},
	year = {2023},
	publisher = {biorxiv},
	doi = {10.1101/2023.08.31.555521 },
	author = {Yan, Shi and Li, Lu and Horner, David and Ebrahimi, Parvaneh and Chawes, Bo and Lars O. {Dragsted} and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42752,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Norwegian Bioinformatics Days, Sundvolden, Norway},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42657,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {NuGOweek 2022 in Spain},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42691,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Nordic Metabolomics 2022, Copenhagen, Denmark},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42755,
	title = {Characterizing postprandial metabolic response using multi-way data analysis},
	year = {2022},
	address = {Norwegian Bioinformatics Days},
	abstract = {Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and\&nbsp;fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.\&nbsp;In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test.\&nbsp;\&nbsp;We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.},
	keywords = {CANDECOMP/PARAFAC, Dynamic metabolomics data, large-scale dataset, Tensor factorization},
	author = {Yan, Shi and Li, Lu and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42834,
	title = {Characterizing postprandial metabolomics response using multi-way data analysis},
	howpublished = {Annual NORBIS Conference},
	year = {2022},
	abstract = {Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.\&nbsp;In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test.\&nbsp; We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.\&nbsp;Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.},
	author = {Yan, Shi and Li, Lu and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42656,
	title = {Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study},
	year = {2022},
	address = {Metabolomics 2022, Valencia, Spain},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
