Giancarlo Mauri

FiCoS is a GPU-powered deterministic simulator for biochemical networks that provides fine- and coarse-grained parallelization, achieving up to 855x speedup over CPU-based simulators.

ACDC automates cell detection and counting in time-lapse microscopy images, achieving high accuracy without large annotated datasets.

This paper integrates single-cell RNA-seq data into metabolic models to characterize tumour cell populations and identifies metabolic heterogeneity and interactions.

MaREA identifies metabolic reactions significantly up-/down-regulated in cancer subgroups using RNAseq data and visualizes results on metabolic maps.

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.

CAPRI infers cancer progression models from cross-sectional data, outperforming existing methods in accuracy and robustness to noise and small sample sizes.

This paper introduces CAPRESE, a framework for inferring cancer progression tree models using probabilistic causation, showing improved performance on synthetic and real datasets.