A database for publications published by researchers and students at SimulaMet.
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- All (359)
- Journal articles (130)
- Books (2)
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- Proceedings, refereed (161)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
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- Posters (7) Remove Posters <span class="counter">(7)</span> filter
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Posters
Characterizing postprandial metabolic response using multi-way data analysis
Norwegian Bioinformatics Days, 2022.Status: Published
Characterizing postprandial metabolic response using multi-way data analysis
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.
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. 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.
Afilliation | Machine Learning |
Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | Norwegian Bioinformatics Days |
Keywords | CANDECOMP/PARAFAC, Dynamic metabolomics data, large-scale dataset, Tensor factorization |
Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study
Metabolomics 2022, Valencia, Spain, 2022.Status: Published
Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study
Afilliation | Machine Learning |
Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | Metabolomics 2022, Valencia, Spain |
Predicting drug exposure in kidney transplanted patients using machine learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Accepted
Predicting drug exposure in kidney transplanted patients using machine learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | NORA Annual Conference, Stavanger, Norway |
Type of Work | Poster presentation |
Automatic Thumbnail Selection for Soccer using Machine Learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Accepted
Automatic Thumbnail Selection for Soccer using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | NORA Annual Conference, Stavanger, Norway |
Posters
An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization
2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, 2021.Status: Published
An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Poster |
Year of Publication | 2021 |
Place Published | 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands |
Assessment of sperm motility according to WHO classification using convolutional neural networks
ESHRE: ESHRE, 2021.Status: Accepted
Assessment of sperm motility according to WHO classification using convolutional neural networks
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2021 |
Publisher | ESHRE |
Place Published | ESHRE |
Understanding the Dynamics of Complex Systems through Time-Evolving Data Mining
SIAM International Conference on Data Mining, 2021.Status: Published
Understanding the Dynamics of Complex Systems through Time-Evolving Data Mining
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Poster |
Year of Publication | 2021 |
Place Published | SIAM International Conference on Data Mining |
Type of Work | Poster at SDM’21 Doctoral Forum |