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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 samples collected either through bulk multi-region sequencing experime
Externí odkaz:
http://arxiv.org/abs/1709.01076
Autor:
Caravagna, Giulio, Ramazzotti, Daniele
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 practical and surprisingly successful solution to this learning problem
Externí odkaz:
http://arxiv.org/abs/1706.02386
Autor:
Patruno, Lucrezia, Galimberti, Edoardo, Ramazzotti, Daniele, Caravagna, Giulio, De Sano, Luca, Antoniotti, Marco, Graudenzi, Alex
The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models of cancer evolution. We here build up on previous works on cancer progr
Externí odkaz:
http://arxiv.org/abs/1705.03067
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 concordant predictions over specific parametrisations, but it is general
Externí odkaz:
http://arxiv.org/abs/1605.02190
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 examples of such diseases, where the mutational load in the cancerous/vira
Externí odkaz:
http://arxiv.org/abs/1602.07857
Autor:
Caravagna, Giulio, Ramazzotti, Daniele
Publikováno v:
In Neurocomputing 11 August 2021 448:48-59
Autor:
Caravagna, Giulio, Graudenzi, Alex, Ramazzotti, Daniele, Sanz-Pamplona, Rebeca, De Sano, Luca, Mauri, Giancarlo, Moreno, Victor, Antoniotti, Marco, Mishra, Bud
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 explanatory models of how the (epi)genomic events are choreographed in can
Externí odkaz:
http://arxiv.org/abs/1509.07918
Autor:
De Sano, Luca, Caravagna, Giulio, Ramazzotti, Daniele, Graudenzi, Alex, Mauri, Giancarlo, Mishra, Bud, Antoniotti, Marco
Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract pop
Externí odkaz:
http://arxiv.org/abs/1509.07304