E
Enrico Ferrero
Researcher at Novartis
Publications - 29
Citations - 2138
Enrico Ferrero is an academic researcher from Novartis. The author has contributed to research in topics: DNA methylation & Medicine. The author has an hindex of 10, co-authored 24 publications receiving 1506 citations. Previous affiliations of Enrico Ferrero include University of Turin & University of Cambridge.
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Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Journal ArticleDOI
Integrating Cardiac PIP3 and cAMP Signaling through a PKA Anchoring Function of p110γ
Alessia Perino,Alessandra Ghigo,Enrico Ferrero,Fulvio Morello,Gaetano Santulli,George S. Baillie,Federico Damilano,Allan J. Dunlop,Catherine T Pawson,Romy Walser,Renzo Levi,Fiorella Altruda,Lorenzo Silengo,Lorene K. Langeberg,Gitte Neubauer,Stephane Heymans,Giuseppe Lembo,Matthias P. Wymann,Reinhard Wetzker,Miles D. Houslay,Guido Iaccarino,John D. Scott,Emilio Hirsch +22 more
TL;DR: In this paper, it was shown that p110γ anchors protein kinase A (PKA) through a site in its N-terminal region, which provides local feedback control of PIP3 and cAMP signaling events.
Journal ArticleDOI
In silico prediction of novel therapeutic targets using gene-disease association data.
TL;DR: The in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process.
Posted ContentDOI
Opportunities And Obstacles For Deep Learning In Biology And Medicine
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Anthony Gitter,Casey S. Greene +26 more
TL;DR: This work examines applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges.
Journal ArticleDOI
The midbody interactome reveals unexpected roles for PP1 phosphatases in cytokinesis
Luisa Capalbo,Zuni I. Bassi,Marco Geymonat,Sofia Todesca,Liviu Copoiu,Anton J. Enright,Giuliano Callaini,Maria Giovanna Riparbelli,Lu Yu,Jyoti S. Choudhary,Jyoti S. Choudhary,Enrico Ferrero,Enrico Ferrero,Sally P. Wheatley,Max E. Douglas,Masanori Mishima,Masanori Mishima,Pier Paolo D'Avino +17 more
TL;DR: The characterization of the intricate midbody protein-protein interaction network (interactome), which identifies many previously unknown interactions and provides an extremely valuable resource for dissecting the multiple roles of the midbody, is reported.