107 citations · 423 across the 17 of their papers we have counts for
20 papers
Mixtures of Neural Cellular Automata: A Stochastic Framework for Growth Modelling and Self-Organization
Salvatore Milite, Giulio Caravagna, Andrea Sottoriva
Neural Cellular Automata (NCAs) are a promising new approach to model self-organizing processes, with potential applications in life science. However, their deterministic nature li…
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…
Learning the structure of Bayesian Networks via the bootstrap
Giulio Caravagna, Daniele Ramazzotti
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a p…
Matching models across abstraction levels with Gaussian Processes
Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively co…
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Daniele Ramazzotti, Alex Graudenzi, Giulio Caravagna +1
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common exam…
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…