Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Dylan Feldner-Busztin"'
Autor:
Zain Alhashem, Dylan Feldner-Busztin, Christopher Revell, Macarena Alvarez-Garcillan Portillo, Karen Camargo-Sosa, Joanna Richardson, Manuel Rocha, Anton Gauert, Tatianna Corbeaux, Martina Milanetto, Francesco Argenton, Natascia Tiso, Robert N Kelsh, Victoria E Prince, Katie Bentley, Claudia Linker
Publikováno v:
eLife, Vol 11 (2022)
Coordination of cell proliferation and migration is fundamental for life, and its dysregulation has catastrophic consequences, such as cancer. How cell cycle progression affects migration, and vice versa, remains largely unknown. We address these que
Externí odkaz:
https://doaj.org/article/46c887effd434e8e951b0000a22ed165
Autor:
Robert Hinch, William J M Probert, Anel Nurtay, Michelle Kendall, Chris Wymant, Matthew Hall, Katrina Lythgoe, Ana Bulas Cruz, Lele Zhao, Andrea Stewart, Luca Ferretti, Daniel Montero, James Warren, Nicole Mather, Matthew Abueg, Neo Wu, Olivier Legat, Katie Bentley, Thomas Mead, Kelvin Van-Vuuren, Dylan Feldner-Busztin, Tommaso Ristori, Anthony Finkelstein, David G Bonsall, Lucie Abeler-Dörner, Christophe Fraser
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 7, p e1009146 (2021)
SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predi
Externí odkaz:
https://doaj.org/article/40c60c6d49f340eba2196f65ed314e7d
Autor:
Dylan Feldner-Busztin, Panos Firbas Nisantzis, Shelley Jane Edmunds, Gergely Boza, Fernando Racimo, Shyam Gopalakrishnan, Morten Tønsberg Limborg, Leo Lahti, Gonzalo G de Polavieja
Publikováno v:
Feldner-Busztin, D, Nisantzis, P F, Edmunds, S J, Boza, G, Racimo, F, Gopalakrishnan, S, Limborg, M T, Lahti, L & de Polavieja, G G 2023, ' Dealing with dimensionality : the application of machine learning to multi-omics data ', Bioinformatics, vol. 39, no. 2, btad021 . https://doi.org/10.1093/bioinformatics/btad021
Motivation Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amou
Autor:
Zain Alhashem, Dylan Feldner-Busztin, Christopher Revell, Macarena Alvarez-Garcillan Portillo, Karen Camargo-Sosa, Joanna Richardson, Manuel Rocha, Anton Gauert, Tatianna Corbeaux, Martina Milanetto, Francesco Argenton, Natascia Tiso, Robert N Kelsh, Victoria E Prince, Katie Bentley, Claudia Linker
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8c2ccace143b129e8b2806e5cb5ecc4
https://doi.org/10.7554/elife.73550.sa2
https://doi.org/10.7554/elife.73550.sa2