10 citations · 12 across the 2 of their papers we have counts for
9 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…
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…
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…
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possi…
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process…
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga +2
The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with…