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Found 7 papers, 4 papers with code
Learning the structure of Bayesian Networks via the bootstrap
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest.
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena.
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse.
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions.
Matching models across abstraction levels with Gaussian Processes
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study.
Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation
The Wivace 2013 Electronic Proceedings in Theoretical Computer Science (EPTCS) contain some selected long and short articles accepted for the presentation at Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation, which was held at the University of Milan-Bicocca, Milan, on the 1st and 2nd of July, 2013.
cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models
cyTRON was developed in Java; the code is available at https://github. com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps. cytoscape. org/apps/cytron.
