Soufiane M. C. Mourragui

The paper introduces sincei, a command-line toolkit for exploring single-cell epigenomic data from aligned reads, aiming to improve accessibility and flexibility in data analysis.

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

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.

SpaGE integrates spatial and scRNA-seq data to predict whole-transcriptome spatial gene expressions, outperforming existing methods and identifying new spatial gene patterns.

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