6th CEN Conference

Session

High dimensional data 3

21 May 2026, 13:45

Room 14

Conveners

High dimensional data 3

• Sarah Friedrich-Welz (University of Augsburg)

77. Integrating functional motif discovery and statistical learning approaches for advanced blood glucose prediction in real-world conditions

Sara Garber (Department of Statistics and Data Science, University of Augsburg)

21/05/2026, 13:45

oral presentation

Functional data analysis has established itself as a powerful framework for analyzing data recorded over continuous domains such as time. Within this context, functional motif discovery refers to the identification of recurrent patterns that appear multiple times across different portions of a single curve and/or within misaligned portions of multiple curves. In this study, we explore the...

162. Statistical end-to-end analysis of large-scale microbial growth data with DGrowthR

Medina Feldl (Institute of Computational Biology, Helmholtz Munich)

21/05/2026, 14:03

oral presentation

Quantitative analysis of microbial growth curves is essential for understanding how bacterial populations respond to environmental cues. Traditional analysis approaches make parametric assumptions about the functional form of these curves, limiting their usefulness for studying conditions that distort standard growth curves. In addition, modern robotics platforms enable the high-throughput...

204. Population Matching to Enhance Back-Translation Between Clinical Trials and Biobank Data for Drug Target Discovery

Han Chiam (Medical University of Vienna)

21/05/2026, 14:21

oral presentation

Population-scale genomic biobanks provide unique opportunities for data-driven drug target discovery. However, these resources often lack detailed data on clinical phenotypes, whereas clinical trials offer rich phenotypic information but are limited in omics coverage and mostly lack genotyping. This imbalance creates gaps in the mechanistic interpretation of clinical findings.
To address...

64. Entropy Adjusted Graphical Lasso for Sparse Precision Matrix Estimation

Vahe Avagyan (Wageningen University & Research)

21/05/2026, 14:39

oral presentation

The estimation of a precision matrix is a crucial problem in various research fields, particularly when working with high dimensional data. In such settings, the most common approach is to use the penalized maximum likelihood. The literature typically employs Lasso, Ridge and Elastic-Net norms, which effectively shrink the entries of the estimated precision matrix. Although these shrinkage...