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...
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...
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,...
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...
