6th CEN Conference

Session

High dimensional data 1

19 May 2026, 10:45

Room 14

Conveners

High dimensional data 1

• Bart Mertens (Leiden University Medical Centre)

187. Unraveling Aging in the CSF Proteome: A Systematic Comparison of Variable-Selection Methods for Protein Risk Score Modeling

Sathish Ravindranth (TU Dortmund University)

19/05/2026, 10:45

oral presentation

Aging is the dominant risk factor for neurodegenerative and systemic diseases, yet its molecular signatures remain obscured within high-dimensional, noisy, and strongly correlated proteomes. To address this challenge, we introduce the Protein Risk Score (ProtRS) framework—a systematic evaluation framework for ProtRS modeling that assesses how different multivariate approaches extract...

196. Detecting gene-environment interactions to guide personalized intervention: boosting distributional regression for polygenic scores

Qiong Wu (University of Marburg)

19/05/2026, 11:03

oral presentation

Polygenic risk scores can be used to model the individual genetic liability for human traits. Current methods primarily focus on modeling the mean of a phenotype neglecting the variance. However, genetic variants associated with phenotypic variance can provide important insights to gene-environment interaction studies. To overcome this, we propose snpboostlss, a cyclical gradient boosting...

135. Regularized Multi-Omics Regression Modelling for Transcriptomic–Proteomic Integration in Mice with induced liver Damage.

Ngoune Darwin (TU Dortmund University)

19/05/2026, 11:21

oral presentation

Regularized Multi-Omics Regression Modeling for Transcriptomic–Proteomic Integration in Mice with induced liver Damage.

Toxicological compounds exert complex effects on tissues and organisms, which can be investigated using genomic, transcriptomic, and proteomic data. A central challenge lies in understanding the relationship between RNA and protein levels. While these are expected to be...

107. StabCell: A stability-selection framework with error control for clustering and differential expression analysis of scRNA-seq data

Niklas Lück (TU Dortmund University, IUF - Leibniz Research Institute for Environmental Medicine)

19/05/2026, 11:39

oral presentation

Single-cell RNA sequencing has given researchers unparalleled insight into biological systems. It enables the identification of distinct cellular subpopulations, the characterization of differences between them, and the assessment of overall tissue heterogeneity. Conventional analysis pipelines first cluster individual cells into similar groups and then test for differentially expressed genes...

43. A Nested Cross-Validation Framework for Leakage-Free Calibration of Adaptive Elastic-Net Regression in High-Dimensional Data

Gul Inan (Koc University)

19/05/2026, 11:57

oral presentation

Penalized regression models such as Lasso, Elastic-Net and their adaptive extensions are widely used for simultaneous variable selection and prediction in high-dimensional data analysis. However, conventional implementations of adaptive Elastic-Net (AdaENet) regression often estimate the adaptive hyper-parameter for the Elastic-Net penalty term using the entire dataset before dividing it into...