Bruno M. Grande

LCR-modules provides a flexible, automated suite of workflows for cancer genome analysis, supporting diverse sequencing types and enabling reproducible, scalable analysis of over 10,800 samples.

This paper introduces a self-training method for gene expression-based cancer classification that works across different data types and normalization methods.

This study identifies genetic drivers of mantle cell lymphoma through exome and genome sequencing, revealing novel mutations in genes involved in RNA splicing and regulation.

This paper identifies and corrects technical biases on sex chromosomes in sequencing data using a new tool called XYalign.

SUBSTRA is a Bayesian method for patient stratification that balances predictive performance and interpretability by using regularized biclustering and iterative feature weighting.

This study identifies somatic coding and regulatory variants in DLBCL, revealing novel mechanisms of oncogene deregulation and potential therapeutic targets.

The paper introduces Galaxy tools for analyzing cancer genomics data, demonstrating their use in discovering new lymphoma-related genes from a cohort of 96 diffuse large B-cell lymphomas.