Luca De Sano

The paper introduces two Cytoscape-based tools, cyTRON and cyTRON/JS, for inferring cancer evolution models from genomic and proteomics data.

TRaIT infers mutational graphs from single-cell and multi-region sequencing data to model tumour evolution, improving accuracy and robustness.

SIMLR is a tool for single-cell RNA-seq analysis that improves clustering and visualization through multi-kernel learning.

OncoScore is a text-mining tool that ranks genes by their association with cancer, achieving high accuracy in prioritizing cancer-related genes.

The paper introduces PiCnIc, a machine learning pipeline to infer cancer progression models from NGS data, demonstrating its application to colorectal cancer and suggesting novel hypotheses.

TRONCO infers cancer progression models from genomic data, providing population-level and individual-level insights into clonal evolution and mutation accumulation.

TRONCO is an R package for cancer progression modeling using cross-sectional genomic data, offering improved algorithms and tools for analysis and visualization.