Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations.

ABSTRACT: Longitudinal 'omics analytical methods are extensively used in the evolving field of precision medicine, by enabling 'big data' recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representa

SUBMITTER: Mor U 

PROVIDER: S-EPMC9328521 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature