Lodewyk F.A. Wessels

This study identifies SMIM1 as a key regulator of an alternative iron uptake pathway through hemoglobin binding and lysosomal iron release, with implications for iron homeostasis and erythropoiesis.

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

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

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.

The paper shows that drug response predictors trained on perturbation response model-generated profiles outperform those trained on baseline or pre-treatment profiles.

The paper introduces scMeMo, a mechanistic modeling framework that infers unobserved variables in single cells using diverse measurements, demonstrating its ability to predict protein levels and cell cycle behavior from limited data.

Percolate combines gene expression with other genomic data to create robust DNA-based predictors of drug response, outperforming individual data types.

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.

The paper uses genomic profiling to show that some invasive breast cancers are clonally unrelated to their preceding DCIS, suggesting DCIS may act as both a precursor and independent risk factor.

This paper introduces Sobolev Alignment, a method to identify shared biological processes between cancer cell lines and tumors using deep generative models and kernel methods, showing its effectiveness on synthetic and real datasets.

The paper develops TRANSACT, a transfer learning framework for predicting clinical drug response, outperforming existing methods on multiple datasets.

PRECISE transfers drug response predictors from pre-clinical models to human tumors using domain adaptation, improving biomarker-drug associations in human tumors.

This paper introduces funcSFA to identify driver biological processes in tumors by integrating multiple data types and functional annotation, finding common and novel processes in breast and lung cancers.

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.

iTOP infers relationships between omics datasets using partial matrix correlations and identifies gene expression as a mediator between other datasets and drug response.

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

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.

This paper develops feedback-ISA (f-ISA) to model feedback mechanisms in signaling pathways, showing it can explain phosphorylation levels in MAPK and AKT pathways across breast cancer cell lines and conditions.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.