Evert Bosdriesz

ESGI: Efficient splitting of generic indices in single-cellsequencing data

ESGI provides a flexible framework for demultiplexing and processing single-cell sequencing data with arbitrary barcode architectures, supporting complex and emerging experimental designs.

Cell-state specific drug-responses are associated with differences in signaling network wiring.

The paper investigates how cell-state differences affect drug responses and signaling network interactions in human epidermal stem cells.

Reconstructing and comparing signal transduction networks from single cell protein quantification data

This paper introduces scCNR, a method to reconstruct signal transduction networks from single-cell protein data by treating stochastic variation as perturbations, and validates it on simulated and experimental data.

Identifying mutant-specific multi-drug combinations using Comparative Network Reconstruction

The paper presents a pipeline to identify effective and selective multi-drug combinations by reconstructing mutant-specific signaling networks and predicting their impact on cell viability.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

Identifying biomarkers of anti-cancer drug synergy using multi-task learning

The paper uses multi-task learning to identify biomarkers of anti-cancer drug synergy using low-sample-size drug combination data from 85 cell lines and 167 drug combinations.

Comparative Network Reconstruction using Mixed Integer Programming

This paper introduces a computational method to reconstruct signaling networks from incomplete perturbation data and identify differences between networks in sensitive and resistant cells.

Modelling Signalling Networks from Perturbation Data

The paper develops a modified MRA method to model signalling networks from incomplete perturbation data and applies it to study SHP2's role in colon cancer cell lines.