Zobrazeno 1 - 10
of 434
pro vyhledávání: '"Michael Bronstein"'
Autor:
Lemberger T; EMBO, Heidelberg, Germany. thomas.lemberger@embo.org.
Publikováno v:
EMBO reports [EMBO Rep] 2024 Nov; Vol. 25 (11), pp. 4629-4633. Date of Electronic Publication: 2024 Sep 23.
Autor:
Joshua Southern, Guadalupe Gonzalez, Pia Borgas, Liam Poynter, Ivan Laponogov, Yoyo Zhong, Reza Mirnezami, Dennis Veselkov, Michael Bronstein, Kirill Veselkov
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiothera
Externí odkaz:
https://doaj.org/article/b764e9a99a7a45ccabebf81b791ca436
Autor:
Ana-Maria Creţu, Federico Monti, Stefano Marrone, Xiaowen Dong, Michael Bronstein, Yves-Alexandre de Montjoye
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
Large amounts of interaction data are collected by messaging apps, mobile phone carriers, and social media. Creţu et al. propose a behavioral profiling attack model and show that the stability of people’s interaction networks over time can allow i
Externí odkaz:
https://doaj.org/article/6ab46f88b14f4ac8a29c9d21b1cebf07
Autor:
Soha Sadat Mahdi, Harold Matthews, Nele Nauwelaers, Michiel Vanneste, Shunwang Gong, Giorgos Bouritsas, Gareth S. Baynam, Peter Hammond, Richard Spritz, Ophir D. Klein, Benedikt Hallgrimsson, Hilde Peeters, Michael Bronstein, Peter Claes
Publikováno v:
IEEE Access, Vol 10, Pp 23450-23462 (2022)
Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets u
Externí odkaz:
https://doaj.org/article/be55dad4767447a29263c8bcfe7746a7
Publikováno v:
Human Genomics, Vol 15, Iss 1, Pp 1-12 (2021)
Abstract Background Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field
Externí odkaz:
https://doaj.org/article/dfeac2dc4c9a4160a761ade1691c4b5a
Autor:
Ivan Laponogov, Guadalupe Gonzalez, Madelen Shepherd, Ahad Qureshi, Dennis Veselkov, Georgia Charkoftaki, Vasilis Vasiliou, Jozef Youssef, Reza Mirnezami, Michael Bronstein, Kirill Veselkov
Publikováno v:
Human Genomics, Vol 15, Iss 1, Pp 1-11 (2021)
Abstract In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were p
Externí odkaz:
https://doaj.org/article/815be78e4c6c4594b59d402e76a7b300
Autor:
BRONSTEIN, MICHAEL
Publikováno v:
Campaigns & Elections (2010). Jun2011, Vol. 32 Issue 304, p11-13. 3p.
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78254ab186329fb9de1885f34b412be7
https://doi.org/10.1145/3534678.3542598
https://doi.org/10.1145/3534678.3542598
Autor:
Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E. Correia
Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae2dd2d3757cef1923a6c645e8be5972
https://doi.org/10.1101/2022.06.16.496402
https://doi.org/10.1101/2022.06.16.496402
Publikováno v:
SAM Research Report, 2022-04
T. Konstantin Rusch
T. Konstantin Rusch
We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework for deep learning on graphs. It is based on discretizations of a second-order system of ordinary differential equations (ODEs), which model a network of nonlinear forced and d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8e0aa4d74cff4231d5a71f3c8b29a3cd
https://hdl.handle.net/20.500.11850/531376
https://hdl.handle.net/20.500.11850/531376