Daniele Ramazzotti

BASCULE uses a Bayesian framework to standardize and cluster mutational signatures across cancer types, identifying prognostic subtypes in over 8,000 samples.

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.

PMCE infers probabilistic graphical models of cancer evolution from mutational profiles, showing a significant correlation between predicted evolutionary paths and patient survival.

SparseSignatures is a framework for discovering mutational signatures in tumor genomes, outperforming existing methods and identifying signatures linked to known mutagenic mechanisms in pancreatic and breast tumors.

The paper analyzes AID-related mutations across cancers and finds they are associated with better outcomes in ICI-treated patients and contribute to immunogenic neoepitopes.

The paper analyzes SARS-CoV-2 genomic diversity to reveal mutational signatures and host responses, showing heterogeneous genomic architectures and the role of purifying selection and transmission dynamics.

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.

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

This paper introduces a machine learning method to detect repeated cancer evolution patterns across patients using multi-region sequencing data, identifying prognostic patient subgroups.

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.

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.

This paper introduces SIMLR, a similarity-learning framework for single-cell RNA-seq data analysis, showing improved clustering and visualization.

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.

This study compares Bayesian heuristics for inferring breast cancer gene networks from experimental data.

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.