Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Luca De Sano"'
Autor:
Diletta Fontana, Ilaria Crespiatico, Valentina Crippa, Federica Malighetti, Matteo Villa, Fabrizio Angaroni, Luca De Sano, Andrea Aroldi, Marco Antoniotti, Giulio Caravagna, Rocco Piazza, Alex Graudenzi, Luca Mologni, Daniele Ramazzotti
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-18 (2023)
Abstract Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics d
Externí odkaz:
https://doaj.org/article/2c8ebc0ac2384c04920b0f140fda5e6f
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-13 (2019)
Abstract 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
Externí odkaz:
https://doaj.org/article/bbd1e2cbdf6a4f3da1bf433dded3846c
Autor:
Daniele Ramazzotti 1, Alex Graudenzi 2, 3, Luca De Sano 2, Marco Antoniotti 2, 4, Giulio Caravagna 5
Publikováno v:
BMC bioinformatics 20 (2019): 210. doi:10.1186/s12859-019-2795-4.
info:cnr-pdr/source/autori:Daniele Ramazzotti 1, Alex Graudenzi 2,3, Luca De Sano 2, Marco Antoniotti 2,4 and Giulio Caravagna 5/titolo:Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data/doi:10.1186%2Fs12859-019-2795-4./rivista:BMC bioinformatics/anno:2019/pagina_da:210/pagina_a:/intervallo_pagine:210/volume:20
info:cnr-pdr/source/autori:Daniele Ramazzotti 1, Alex Graudenzi 2,3, Luca De Sano 2, Marco Antoniotti 2,4 and Giulio Caravagna 5/titolo:Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data/doi:10.1186%2Fs12859-019-2795-4./rivista:BMC bioinformatics/anno:2019/pagina_da:210/pagina_a:/intervallo_pagine:210/volume:20
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:
https://explore.openaire.eu/search/publication?articleId=cnr_________::95d44c3af96e4224fb43f1de89642b43
https://publications.cnr.it/doc/402392
https://publications.cnr.it/doc/402392
Autor:
Carlo Gambacorti-Passerini, Rocco Piazza, Roberta Spinelli, Daniele Ramazzotti, Pierangelo Ferrari, Nicoletta Cordani, Alessandra Pirola, Luca De Sano, Nitesh Sharma, Vera Magistroni
Publikováno v:
Scientific Reports
The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramoun
MotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::146a0a720469da632d22e5d7248b3b7b
Autor:
Nicoletta Cordani, Pierangelo Ferrari, Alessandra Pirola, Rocco Piazza, Luca De Sano, Vera Magistroni, Roberta Spinelli, Nitesh Sharma, Daniele Ramazzotti, Carlo Gambacorti-Passerini
The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramoun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96314b87e1c0d9748bb9de8aa8ceed72
https://doi.org/10.1101/115329
https://doi.org/10.1101/115329
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 from expression data observed for heterogenous samples, is presented here. SIMLR can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c708af17fa83d369b2f8d8a9f36b298
Autor:
Giancarlo Mauri, Marco Antoniotti, Alex Graudenzi, Luca De Sano, Rebeca Sanz-Pamplona, Giulio Caravagna, Victor Moreno, Bud Mishra, Daniele Ramazzotti
Publikováno v:
Caravagna, G, Graudenzi, A, Ramazzotti, D, Sanz-Pamplona, R, De Sano, L, Mauri, G, Moreno, V, Antoniotti, M & Mishra, B 2016, ' Algorithmic methods to infer the evolutionary trajectories in cancer progression ', Proceedings of the National Academy of Sciences, vol. 113, no. 28, pp. E4025-E4034 . https://doi.org/10.1073/pnas.1520213113
Proceedings of the National Academy of Sciences of the United States of America 113 (2016): E4025–E4034.
info:cnr-pdr/source/autori:Caravagna, Giulio; Graudenzi, Alex; Ramazzotti, Daniele; Sanz-Pamplona, Rebeca; De Sano, Luca; Mauri, Giancarlo; Moreno, Victor; Antoniotti, Marco; Mishra, Bud/titolo:Algorithmic methods to infer the evolutionary trajectories in cancer progression/doi:/rivista:Proceedings of the National Academy of Sciences of the United States of America/anno:2016/pagina_da:E4025/pagina_a:E4034/intervallo_pagine:E4025–E4034/volume:113
Proceedings of the National Academy of Sciences of the United States of America 113 (2016): E4025–E4034.
info:cnr-pdr/source/autori:Caravagna, Giulio; Graudenzi, Alex; Ramazzotti, Daniele; Sanz-Pamplona, Rebeca; De Sano, Luca; Mauri, Giancarlo; Moreno, Victor; Antoniotti, Marco; Mishra, Bud/titolo:Algorithmic methods to infer the evolutionary trajectories in cancer progression/doi:/rivista:Proceedings of the National Academy of Sciences of the United States of America/anno:2016/pagina_da:E4025/pagina_a:E4034/intervallo_pagine:E4025–E4034/volume:113
The evolutionary nature of cancer relates directly to a renewed focus on the voluminous NGS (next generation sequencing) data, aiming at the identification of explanatory models of how the (epi)genomic events are choreographed in cancer initiation an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3f74420fe5ba8ba1c07a5074f2a1dc7
http://hdl.handle.net/2445/193443
http://hdl.handle.net/2445/193443
Autor:
Giancarlo Mauri, Daniele Ramazzotti, Alex Graudenzi, Luca De Sano, Giulio Caravagna, Bud Mishra, Marco Antoniotti
Publikováno v:
Scopus-Elsevier
Antoniotti, M, Caravagna, G, De Sano, L, Graudenzi, A, Mauri, G, Mishra, B & Ramazzotti, D 2016, ' Design of the TRONCO BioConductor Package for TRanslational ONCOlogy ', The R Journal, vol. 8, no. 2, pp. 39-59 . https://doi.org/10.1101/027524
Antoniotti, M, Caravagna, G, De Sano, L, Graudenzi, A, Mauri, G, Mishra, B & Ramazzotti, D 2016, ' Design of the TRONCO BioConductor Package for TRanslational ONCOlogy ', The R Journal, vol. 8, no. 2, pp. 39-59 . https://doi.org/10.1101/027524
Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patiens (cro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc578d69a135bc36547d97edf785faf9
Publikováno v:
BMC Bioinformatics
Background: Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b28756dfa6ad68c0e3bb4ceddae68a60
http://hdl.handle.net/11368/2956348
http://hdl.handle.net/11368/2956348