Eike Staub

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

CLING is a multi-view factor model that improves factor and loading recovery in multi-omic data by using nonparametric Gamma shrinkage, outperforming baselines on synthetic and real datasets.

Cellohood identifies cellular neighborhoods in spatial transcriptomics data, revealing clinically relevant patterns across multiple granularity levels.

The paper shows that stromal cells, particularly fibroblasts, dominate EMT-related gene expression signature scores in cancer, rather than tumor-intrinsic properties.

The paper develops immune cell type signatures using multiple datasets and applies random forest classification to improve cell type assignment in single-cell RNA-seq data.

SLIDE-VIP identifies synthetic lethal gene pairs in DNA damage repair, chromatin remodeling, and cell cycle using multi-omics data and eight statistical tests, reducing candidate pairs by 250-fold.

CONET infers tumor evolutionary trees and copy number calls from single-cell data using a probabilistic model with MCMC and regularization.

The paper develops a deep learning model called DEERS to predict kinase inhibitor efficacy in cancer cell lines, achieving high accuracy and providing interpretable insights into drug response mechanisms.

The paper evaluates feature selection strategies for drug sensitivity prediction, finding that prior knowledge-based methods outperform genome-wide approaches for drugs targeting specific genes and pathways.