Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Lorenzo Pantolini"'
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 1, p e1007900 (2021)
The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilit
Externí odkaz:
https://doaj.org/article/afd0edf56de94533a704b4e1a8f81fbc
Autor:
Hugo Schweke, Qifang Xu, Gerardo Tauriello, Lorenzo Pantolini, Torsten Schwede, Frédéric Cazals, Alix Lhéritier, Juan Fernandez-Recio, Luis Ángel Rodríguez-Lumbreras, Ora Schueler-Furman, Julia K. Varga, Brian Jiménez-García, Manon F. Réau, Alexandre Bonvin, Castrense Savojardo, Pier-Luigi Martelli, Rita Casadio, Jérôme Tubiana, Haim Wolfson, Romina Oliva, Didier Barradas-Bautista, Tiziana Ricciardelli, Luigi Cavallo, Česlovas Venclovas, Kliment Olechnovič, Raphael Guerois, Jessica Andreani, Juliette Martin, Xiao Wang, Daisuke Kihara, Anthony Marchand, Bruno Correia, Xiaoqin Zou, Sucharita Dey, Roland Dunbrack, Emmanuel Levy, Shoshana Wodak
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adaf2d977a6fae24f92d44626aa47f9a
https://doi.org/10.22541/au.167569565.51141128/v1
https://doi.org/10.22541/au.167569565.51141128/v1
Language models are now routinely used for text classification and generative tasks. Recently, the same architectures were applied to protein sequences, unlocking powerful tools in the bioinformatics field. Protein language models (pLMs) generate hig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::254409b500115b7c56a7925467b2d63e
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 17, Iss 1, p e1007900 (2021)
PLoS Computational Biology, Vol 17, Iss 1, p e1007900 (2021)
The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73fcc985e1b8ddd07e3f9fe4a1a12613
http://europepmc.org/articles/PMC7872233
http://europepmc.org/articles/PMC7872233