Výsledky vyhledávání - "Lucrezia Patruno"

Akademický článek

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing.

Autor: Lucrezia Patruno, Salvatore Milite, Riccardo Bergamin, Nicola Calonaci, Alberto D'Onofrio, Fabio Anselmi, Marco Antoniotti, Alex Graudenzi, Giulio Caravagna

Publikováno v: PLoS Computational Biology, Vol 19, Iss 11, p e1011557 (2023)

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these

Externí odkaz: https://doaj.org/article/711143dbbcf84508bb43f4555ba5999a

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Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data

Autor: Davide Maspero, Fabrizio Angaroni, Lucrezia Patruno, Daniele Ramazzotti, David Posada, Alex Graudenzi

Publikováno v: Communications in Computer and Information Science ISBN: 9783031311826

In recent years, many algorithmic strategies have been developed to exploit single-cell mutational profiles generated via sequencing experiments of cancer samples and return reliable models of cancer evolution. Here, we introduce the COB-tree algorit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1278554b6fa005c7ab5027db1c7055e2
https://hdl.handle.net/10281/414136

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A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations

Autor: Salvatore Milite, Riccardo Bergamin, Lucrezia Patruno, Nicola Calonaci, Giulio Caravagna

MotivationCancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7236e1d5fc1d331a4101d7919ede05e
https://doi.org/10.1101/2021.02.02.429335

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Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems

Autor: Alex Graudenzi, Francesco Craighero, Davide Maspero, Chiara Damiani, Lucrezia Patruno

Publikováno v: Information and computation
281 (2021): 104798. doi:10.1016/j.ic.2021.104798
info:cnr-pdr/source/autori:Patruno L.; Craighero F.; Maspero D.; Graudenzi A.; Damiani C./titolo:Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems/doi:10.1016%2Fj.ic.2021.104798/rivista:Information and computation (Print)/anno:2021/pagina_da:104798/pagina_a:/intervallo_pagine:104798/volume:281

The strong nonlinearity of large and highly connected reaction systems, such as metabolic networks, hampers the determination of variations in reaction fluxes from variations in species abundances, when comparing different steady states of a given sy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2df414818a640685c04472a4f8e6d75c
http://hdl.handle.net/10281/328287

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A review of computational strategies for denoising and imputation of single-cell transcriptomic data

Autor: Davide Maspero, Marco Antoniotti, Fabrizio Angaroni, Lucrezia Patruno, Francesco Craighero, Alex Graudenzi

Publikováno v: Briefings in bioinformatics 22 (2021): bbaa222. doi:10.1093/bib/bbaa222
info:cnr-pdr/source/autori:Patruno L.; Maspero M.; Craighero F.; Angaroni F.; Antoniotti M.; Graudenzi A./titolo:A review of computational strategies for denoising and imputation of single-cell transcriptomic data/doi:10.1093%2Fbib%2Fbbaa222/rivista:Briefings in bioinformatics/anno:2021/pagina_da:bbaa222/pagina_a:/intervallo_pagine:bbaa222/volume:22

Motivation The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing exp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31c46eeb67b55540b8b7ebc623a467b2
https://doi.org/10.1093/bib/bbaa222

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A closed-loop optimization framework for personalized cancer therapy design

Autor: Alex Graudenzi, Mattia Pennati, Davide Maspero, Marco Antoniotti, Fabrizio Angaroni, Lucrezia Patruno

Publikováno v: CIBCB

A current challenge in cancer research is the development of therapeutic strategies aimed at reducing the toxicity of treatments, since Adverse Events (AEs) typically cause substantial problems and long-term damages to the patients. A possible soluti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b505e810ff855777846998444e6e52a4
http://hdl.handle.net/10281/298078

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