C
Clemens Meyer
Researcher at Google
Publications - 14
Citations - 15281
Clemens Meyer is an academic researcher from Google. The author has contributed to research in topics: Segmentation & Protein structure prediction. The author has an hindex of 9, co-authored 14 publications receiving 2916 citations.
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Journal ArticleDOI
Highly accurate protein structure prediction with AlphaFold
John M. Jumper,Richard O. Evans,Alexander Pritzel,Tim Green,Michael Figurnov,Olaf Ronneberger,Kathryn Tunyasuvunakool,Russell Bates,Augustin Žídek,Anna Potapenko,Alex Bridgland,Clemens Meyer,Simon A. A. Kohl,Andrew J. Ballard,Andrew Cowie,Bernardino Romera-Paredes,Stanislav Nikolov,R. D. Jain,Jonas Adler,Trevor Back,Stig Petersen,David Reiman,Ellen Clancy,Michal Zielinski,Martin Steinegger,Michalina Pacholska,Tamas Berghammer,Sebastian Bodenstein,David L. Silver,Oriol Vinyals,Andrew W. Senior,Koray Kavukcuoglu,Pushmeet Kohli,Demis Hassabis +33 more
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Journal ArticleDOI
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw,Joseph R. Ledsam,Bernardino Romera-Paredes,Stanislav Nikolov,Nenad Tomasev,Sam Blackwell,Harry Askham,Xavier Glorot,Brendan O'Donoghue,Daniel Visentin,George van den Driessche,Balaji Lakshminarayanan,Clemens Meyer,Faith Mackinder,Simon Bouton,Kareem Ayoub,Reena Chopra,Dominic King,Alan Karthikesalingam,Cian Hughes,Rosalind Raine,Julian Hughes,Dawn A Sim,Catherine A Egan,Adnan Tufail,Hugh Montgomery,Demis Hassabis,Geraint Rees,Trevor Back,Peng T. Khaw,Mustafa Suleyman,Julien Cornebise,Pearse A. Keane,Olaf Ronneberger +33 more
TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
Journal ArticleDOI
Highly accurate protein structure prediction for the human proteome
Kathryn Tunyasuvunakool,Jonas Adler,Zachary Wu,Tim Green,Michal Zielinski,Augustin Žídek,Alex Bridgland,Andrew Cowie,Clemens Meyer,Agata Laydon,Sameer Velankar,Gerard J. Kleywegt,Alex Bateman,Richard Evans,Alexander Pritzel,Michael Figurnov,Olaf Ronneberger,Russell Bates,Simon A. A. Kohl,Anna Potapenko,Andrew J. Ballard,Bernardino Romera-Paredes,Stanislav Nikolov,R. D. Jain,Ellen Clancy,David Reiman,Stig Petersen,Andrew W. Senior,Koray Kavukcuoglu,Ewan Birney,Pushmeet Kohli,John M. Jumper,Demis Hassabis +32 more
TL;DR: The AlphaFold2 dataset as discussed by the authors is a large-scale and high-accuracy structure prediction dataset for protein structures, which is used to evaluate the structural properties of proteins.
Journal ArticleDOI
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomasev,Xavier Glorot,Jack W. Rae,Michal Zielinski,Harry Askham,Andre Saraiva,Anne Mottram,Clemens Meyer,Suman V. Ravuri,Ivan Protsyuk,Alistair Connell,Cian Hughes,Alan Karthikesalingam,Julien Cornebise,Hugh Montgomery,Geraint Rees,Chris Laing,Clifton R. Baker,Kelly S. Peterson,Ruth M. Reeves,Demis Hassabis,Dominic King,Mustafa Suleyman,Trevor Back,Christopher Nielson,Christopher Nielson,Joseph R. Ledsam,Shakir Mohamed +27 more
TL;DR: A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.
Posted Content
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl,Bernardino Romera-Paredes,Clemens Meyer,Jeffrey De Fauw,Joseph R. Ledsam,Klaus H. Maier-Hein,S. M. Ali Eslami,Danilo Jimenez Rezende,Olaf Ronneberger +8 more
TL;DR: A generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses and reproduces the possible segmentation variants as well as the frequencies with which they occur significantly better than published approaches.