Cargando…
Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis
Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining...
Autores principales: | van Riel, Natal A. W., Tiemann, Christian A., Hilbers, Peter A. J., Groen, Albert K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921164/ https://www.ncbi.nlm.nih.gov/pubmed/33665185 http://dx.doi.org/10.3389/fbioe.2020.536957 |
Ejemplares similares
-
Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions
por: Tiemann, Christian A., et al.
Publicado: (2013) -
AgNPs Aggravated Hepatic Steatosis, Inflammation, Oxidative Stress, and Epigenetic Changes in Mice With NAFLD Induced by HFD
por: Wen, Ling, et al.
Publicado: (2022) -
A Machine Learning Approach for Tracing Tumor Original Sites With Gene Expression Profiles
por: Liang, Xin, et al.
Publicado: (2020) -
Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers
por: Dias-Audibert, Flávia Luísa, et al.
Publicado: (2020) -
A Computational Model for the Analysis of Lipoprotein Distributions in the Mouse: Translating FPLC Profiles to Lipoprotein Metabolism
por: Sips, Fianne L. P., et al.
Publicado: (2014)