6th CEN Conference

Session

TC2: Interpretable Bayesian modelling of highdimensional / complex problems in molecular biomedicine

19 May 2026, 13:45

Room 1 B

Conveners

TC2: Interpretable Bayesian modelling of highdimensional / complex problems in molecular biomedicine

• Manuela Zucknick (University of Oslo)

45. Probabilistic Variable Importance: A Bayesian Perspective on Interpretable Machine Learning

Christian Staerk (IUF – Leibniz Research Institute for Environmental Medicine & TU Dortmund University)

19/05/2026, 13:45

oral presentation

Understanding the relative importance of genetic, molecular and environmental factors is crucial for interpretable prediction models in biomedicine and for targeted prevention. While classical regression-based approaches provide direct interpretability through model coefficients, flexible machine learning (ML) approaches such as random forests and neural networks typically rely on post-hoc...

32. An interpretable varying coefficients approach to non-linear regression

Francesco Stingo (affiliation: University of Florence)

19/05/2026, 14:03

oral presentation

Non-linear regression models are flexible approaches used to model complex associations. In many recent proposals, additional flexibility comes at the cost of loss of interpretability of the model's parameters and, consequently, of the data analysis results. We introduce a flexible model whose parameters are easily interpretable. In particular, the model incorporates non-linear effects through...

12. A modelling framework for detecting and leveraging node-level information in Bayesian network inference

Hélène Ruffieux (University of Cambridge)

19/05/2026, 14:21

oral presentation

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance,...

19. Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis

Zhi Zhao (University of Oslo)

19/05/2026, 14:39

oral presentation

Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, there is no statistical model to...

248. Discussant

Manuela Zucknick

19/05/2026, 14:57

oral presentation