Antoine de Weck

This study identifies METTL1 gene dosage and catalytic activity as therapeutic targets in pediatric cancers, showing that inhibiting METTL1 synergizes with BCL-xL inhibitors.

This paper introduces CRISPR-PspCas13b as a method to systematically silence oncogenic fusion transcripts, showing effectiveness against drug-resistant cancer drivers.

This study benchmarks deep learning and feature engineering methods for analyzing high-content microscopy images to predict phenotypes and cluster cells without manual annotations.

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

TRAWLING simplifies aberrant splicing detection by providing a splicing-aware transcriptome reference, improving RNAseq analysis across multiple datasets.

APSiC identifies novel cancer genes and non-genetic drivers from perturbation screens, revealing context-dependent oncogenic mechanisms and functional roles in specific cancers.

This paper introduces a fully unsupervised deep learning method to cluster cellular images by Mode-of-Action using only pixel intensity values, achieving high accuracy on a labeled dataset.

The paper introduces two methods to correct false positives in CRISPR screens caused by copy number variations, reducing false positives by 70-80% in amplified regions.

The paper identifies a subset of cancer cell lines that are misclassified as fibroblasts based on genetic and genomic analysis.