Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Regis Loeb"'
Autor:
Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y. Durand, Alberto Romagnoni
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned repr
Externí odkaz:
https://doaj.org/article/968f562e2eaa473a80c0d6576df24c54
Autor:
Wouter Heyndrickx, Adam Arany, Jaak Simm, Anastasia Pentina, Noé Sturm, Lina Humbeck, Lewis Mervin, Adam Zalewski, Martijn Oldenhof, Peter Schmidtke, Lukas Friedrich, Regis Loeb, Arina Afanasyeva, Ansgar Schuffenhauer, Yves Moreau, Hugo Ceulemans
Publikováno v:
Artificial Intelligence in the Life Sciences, Vol 3, Iss , Pp 100070- (2023)
In a drug discovery setting, pharmaceutical companies own substantial but confidential datasets. The MELLODDY project developed a privacy-preserving federated machine learning solution and deployed it at an unprecedented scale. Each partner built mod
Externí odkaz:
https://doaj.org/article/c22676311fae4ecf96bc8d92b6788cad
Autor:
Wouter Heyndrickx, Lewis Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, Peter Schmidtke, Nikolas Fechner, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balasz Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher Luscombe, Darren Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen Pickett, Gergely Acs, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans
Federated multi-partner machine learning can be an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource intensive. In the l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8072552725a14731d649af1b8aedc958
https://doi.org/10.26434/chemrxiv-2022-ntd3r
https://doi.org/10.26434/chemrxiv-2022-ntd3r
Autor:
Wouter Heyndrickx, Adam Arany, Jaak Simm, Anastasia Pentina, Noe Sturm, Lina Humbeck, Lewis Mervin, Adam Zalewski, Martijn Oldenhof, Peter Schmidtke, Lukas Friedrich, Regis Loeb, Arina Afanasyeva, Yves Moreau, Hugo Ceulemans
As training volume increases predictive model quality, leveraging existing external data sources holds the promise of time- and cost-efficiency. In a drug discovery setting, pharmaceutical companies all own substantial but confidential datasets. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f77674e1a81c93db41d413ae8829209
https://doi.org/10.26434/chemrxiv-2022-j3xfk
https://doi.org/10.26434/chemrxiv-2022-j3xfk
Publikováno v:
Vrije Universiteit Brussel
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::10b02417aa1f3c2cfbcb04a882e18954
https://cris.vub.be/portal/en/publications/privacy-preserving-reinforcement-learning-over-distributed-datasets(72949f89-66fa-4b5f-ad7d-8dce3ef4c89c).html
https://cris.vub.be/portal/en/publications/privacy-preserving-reinforcement-learning-over-distributed-datasets(72949f89-66fa-4b5f-ad7d-8dce3ef4c89c).html