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(Convex) level sets integration
JP Crouzeix, A Eberhard, D Ralph
Aug 18, 2015
The paper addresses the problem of recovering a pseudoconvex function from the normal cones to its level sets that we call the convex level sets integration problem. An important application is the revealed preference problem....
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Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma
Thanos P. Mourikis, Lorena Benedetti, Elizabeth Foxall, Damjan Temelkovski, Joel Nulsen, Juliane Perner, Matteo Cereda, Jesper Lagergren, Michael Howell, Christopher Yau, Rebecca C. Fitzgerald, Paola Scaffidi, Francesca D. Ciccarelli, Ayesha Noorani, Paul A. W. Edwards, Rachael Fels Elliott, Nicola Grehan, Barbara Nutzinger, Caitriona Hughes, Elwira Fidziukiewicz, Jan Bornschein, Shona MacRae, Jason Crawte, Alex Northrop, Gianmarco Contino, Xiaodun Li, Rachel de la Rue, Annalise Katz-Summercorn, Sujath Abbas, Daniel Loureda, Maria O’Donovan, Ahmad Miremadi, Shalini Malhotra, Monika Tripathi, Simon Tavaré, Andy G. Lynch, Matthew Eldridge, Maria Secrier, Lawrence Bower, Ginny Devonshire, Sriganesh Jammula, Jim Davies, Charles Crichton, Nick Carroll, Peter Safranek, Andrew Hindmarsh, Vijayendran Sujendran, Stephen J. Hayes, Yeng Ang, Andrew Sharrocks, Shaun R. Preston, Sarah Oakes, Izhar Bagwan, Vicki Save, Richard J. E. Skipworth, Ted R. Hupp, J. Robert O’Neill, Olga Tucker, Andrew Beggs, Philippe Taniere, Sonia Puig, Timothy J. Underwood, Robert C. Walker, Ben L. Grace, Hugh Barr, Neil Shepherd, Oliver Old, James Gossage, Andrew Davies, Fuju Chang, Janine Zylstra, Ula Mahadeva, Vicky Goh, Grant Sanders, Richard Berrisford, Catherine Harden, Mike Lewis, Ed Cheong, Bhaskar Kumar, Simon L. Parsons, Irshad Soomro, Philip Kaye, John Saunders, Laurence Lovat, Rehan Haidry, Laszlo Igali, Michael Scott, Sharmila Sothi, Sari Suortamo, Suzy Lishman, George B. Hanna, Christopher J. Peters, Krishna Moorthy, Anna Grabowska, Richard Turkington, Damian McManus, David Khoo, Will Fickling
Jul 14, 2020
Abstract: The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning...
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