Biology subjects

Buehlmann, P.

Publications and source records attributed to Buehlmann, P..

3 recordsLinked to original sources

bioRxiv2026

Drug-Prot: A query system for statistical inference of drug effects and interactions in dynamic proteomic networks

Understanding drug effects and drug-drug interactions is essential for developing combination therapies. We present Drug-Prot, a computational framework that leverages large-scale perturbation proteomics to quantify causal drug effects, drug-drug interactions, and dynamic protein relationships. Using data from 63 single drugs and 59 drug combinations applied to 18 breast cancer cell lines at 6, 24, and 48 hours, Drug-Prot estimates drug effects on protein expression and reconstructs directed temporal protein dependency networks. The publicly available software enables targeted analyses of user-defined protein sets, substantially reducing the multiple-testing burden. Through an interactive web application, users obtain corrected p-values for single-drug and combination effects, directed temporal dependency networks, and downloadable results without requiring access to the underlying proteomic dataset. As a use case, we apply invariance-regularized Random Forests to triple-negative breast cancer cell lines to identify proteins associated with drug response. Querying these proteins in Drug-Prot reveals drug-specific and interaction effects at the protein-network level, illustrating how the framework links candidate causal protein features to actionable drug combinations.

bioinformatics↗

bioRxiv2026

Signal, Bounds, and Baselines: Principles for Rigorous Evaluation of High-Dimensional Biological Perturbation Prediction

Foundation models and deep learning systems are increasingly proposed as core components of "virtual cells" capable of forecasting transcriptomic responses to unseen perturbations. Yet rigorous evaluation of perturbation prediction in high-dimensional gene expression space remains challenging, raising concerns of reported performances. Here, we introduce the SBB principles (Signal, Bounds, and Baselines) for evaluating biological perturbation prediction. The Signal pillar introduces diagnostic meta-metrics and promotes differentially expressed gene (DEG)-based weighting or filtering to verify and recover metric sensitivity to biological signal masked by high-dimensional noise. The Bounds pillar provides perturbation-wise metric calibration against empirical reference points, transforming raw metric values into interpretable quantities that clarify the actual scale of model improvements. The Baselines pillar establishes a hierarchy of interpretable linear models that serve as rigorous performance floors. Applying these principles across seven transcriptomic perturbation datasets, including single and double perturbations, we demonstrate that complex deep learning methods, including foundation models, often still fail to meaningfully surpass simple linear baselines, and that substantial room for improvement remains even where they do. These principles provide a critical standard for distinguishing genuine biological signal from statistical artifacts and for guiding more robust model development.

bioinformatics↗

bioRxiv2021

Identifying cancer pathway dysregulations using differential causal effects

Signaling pathways control cellular behavior. Dysregulated pathways, for example due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, artificial signals from, for example, batch effects have less influence on the result and dce has a higher chance to detect the biological signals. We show that dce outperforms competing methods on synthetic data sets and on CRISPR knockout screens. In an exploratory analysis on breast cancer data from TCGA, we recover known and discover new genes involved in breast cancer progression.

bioinformatics↗