Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Christel Sirocchi"'
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss S4, Pp 1-18 (2024)
Abstract Background Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores t
Externí odkaz:
https://doaj.org/article/f6cf7ddc88dd44a38de98c32ec4dd37a
Autor:
Christel Sirocchi, Alessandro Bogliolo
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-21 (2022)
Abstract Gossip algorithms are message-passing schemes designed to compute averages and other global functions over networks through asynchronous and randomised pairwise interactions. Gossip-based protocols have drawn much attention for achieving rob
Externí odkaz:
https://doaj.org/article/0508f4ce52254a579599610199f1c55c
Publikováno v:
IEEE Access, Vol 10, Pp 54681-54696 (2022)
Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is signific
Externí odkaz:
https://doaj.org/article/11d5251fc0e04d44b1e09f37af93e9a6
Publikováno v:
Selega, A, Sirocchi, C, Iosub, I, Granneman, S & Sanguinetti, G 2017, ' Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments ', Nature Methods, vol. 14, no. 1, pp. 83-89 . https://doi.org/10.1038/nmeth.4068
BUM-HMM is a statistically robust modeling pipeline for interpreting high-throughput RNA structure probing data, including that from transcriptome-wide experiments. Structure probing coupled with high-throughput sequencing could revolutionize our und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24fa941766e711e2699a3e7c03d76ea5
http://hdl.handle.net/20.500.11767/117325
http://hdl.handle.net/20.500.11767/117325
