Magali Richard

This paper benchmarks deconvolution algorithms for multi-omic data, identifying their strengths and limitations across different conditions and data types.

This paper develops a single-nucleus multimodal framework to analyze transcriptional and histone modification landscapes in ovarian cancer, revealing epigenomic priming of chemoresistant states and its impact on treatment outcomes.

HDMAX2-surv identifies epigenetic mediators of survival outcomes in pancreatic cancer using a two-step framework with latent factor modeling and survival models.

The paper develops an immunohistochemical panel to assess intra- and inter-tumoral heterogeneity in PDAC, identifying four distinct phenotypes with clinical implications.

DECONbench benchmarks deconvolution methods for quantifying tumor cell-type heterogeneity by providing datasets, tools, and performance evaluation.

This study evaluates DNA methylation deconvolution methods for cell-type proportion inference, identifies critical steps for accuracy, and develops a benchmark pipeline.

This study measures the proliferation rates of yeast gene deletion mutants under periodic salinity stress, identifying genes that influence growth in fluctuating conditions.

The paper uses dynamic modeling of gene networks to interpret natural DNA variants by personalizing model parameters based on genotype, revealing how variants affect molecular regulation in yeast.

This paper introduces a method to detect genetic variants with probabilistic effects on cellular traits using single-cell data, showing improved detection of linkages missed by classical genetic mapping.