Marco Antoniotti

The paper analyzes TET2 gene variants in Pakistani AML patients and controls, identifying mutational hotspots and evolutionary differences.

This paper introduces a framework for integrating multiomic single-cell data using Wasserstein GANs, enabling analysis of more than two modalities with reduced computational burden.

CONGAS+ is a Bayesian method that maps single-cell RNA and ATAC data to copy number clones, revealing subclonal architectures and genotype-phenotype relationships in cancer.

This study identifies cancer type-specific evolutionary signatures using genomic data from over 35,000 patients, revealing patterns of driver mutation acquisition linked to prognosis.

This study identifies low-frequency mutations in SARS-CoV-2 deep sequencing data to detect variants earlier than consensus-based methods, using 220,788 samples.

The paper uses variant calling from scRNA-seq data to identify potential sample swaps between patient-derived cell lines.

The paper analyzes SARS-CoV-2 synonymous mutations across 400,000 samples, showing that mutations increasing codon similarity to human codons tend to persist, suggesting adaptation to human host codon usage.

This paper introduces LACE, a framework for analyzing longitudinal single-cell mutation data to model cancer evolution and therapy response.

VERSO improves viral phylogenetic inference and quantifies intra-host diversity using clonal variant profiles and variant frequency patterns, outperforming existing tools on simulations and real data.

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

This paper applies optimal control theory to design personalized cancer therapies, showing that individual pharmacokinetic differences significantly impact treatment outcomes.

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

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

This paper introduces Suppes-Bayes Causal Networks to model the accumulation of DNA changes in diseases like cancer and HIV, showing their ability to infer causal relationships from genetic data.

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.

This paper develops a multiscale model to study how stochastic differentiation contributes to intestinal crypt homeostasis and tumor development.