124 citations · 588 across the 20 of their papers we have counts for
4 papers · 1 filter
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Daniele Ramazzotti, Alex Graudenzi, Luca De Sano +2
Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple…
cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models
Lucrezia Patruno, Edoardo Galimberti, Daniele Ramazzotti +4
The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models o…
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
Bo Wang, Daniele Ramazzotti, Luca De Sano +3
We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure f…
Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression
Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti +6
The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of expl…