Michael Bohlke-Schneider
Michael Bohlke-Schneider
Senior Machine Learning Scientist at Amazon
Verified email at amazon.com
Title
Cited by
Cited by
Year
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
A Belsom, M Schneider, L Fischer, O Brock, J Rappsilber
Molecular & Cellular Proteomics 15 (3), 1105-1116, 2016
712016
X‐ray vs. NMR structures as templates for computational protein design
M Schneider, X Fu, AE Keating
Proteins: Structure, Function, and Bioinformatics 77 (1), 97-110, 2009
562009
Protein tertiary structure by crosslinking/mass spectrometry
M Schneider, A Belsom, J Rappsilber
Trends in biochemical sciences 43 (3), 157-169, 2018
462018
An integrated workflow for crosslinking mass spectrometry
ML Mendes, L Fischer, ZA Chen, M Barbon, FJ O'reilly, SH Giese, ...
Molecular systems biology 15 (9), e8994, 2019
362019
Combining physicochemical and evolutionary information for protein contact prediction
M Schneider, O Brock
PloS one 9 (10), e108438, 2014
282014
Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
M Schneider, A Belsom, J Rappsilber, O Brock
Proteins: Structure, Function, and Bioinformatics 84, 152-163, 2016
262016
EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction
K Stahl, M Schneider, O Brock
BMC bioinformatics 18 (1), 303, 2017
242017
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 2020
232020
Gluonts: Probabilistic time series models in python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
192019
Blind evaluation of hybrid protein structure analysis methods based on cross-linking
A Belsom, M Schneider, O Brock, J Rappsilber
Trends in biochemical sciences 41 (7), 564-567, 2016
182016
RBO Aleph: leveraging novel information sources for protein structure prediction
M Mabrouk, I Putz, T Werner, M Schneider, M Neeb, P Bartels, O Brock
Nucleic acids research 43 (W1), W343-W348, 2015
182015
The structure of active opsin as a basis for identification of GPCR agonists by dynamic homology modelling and virtual screening assays
M Schneider, S Wolf, J Schlitter, K Gerwert
FEBS letters 585 (22), 3587-3592, 2011
152011
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus
Advances in Neural Information Processing Systems, 6827-6837, 2019
142019
Blind testing cross-linking/mass spectrometry under the auspices of the 11 th critical assessment of methods of protein structure prediction (CASP11)
A Belsom, M Schneider, L Fischer, M Mabrouk, K Stahl, O Brock, ...
Wellcome open research 1, 2016
132016
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240, 2020
92020
In Situ Structural Restraints from Cross-Linking Mass Spectrometry in Human Mitochondria
PSJ Ryl, M Bohlke-Schneider, S Lenz, L Fischer, L Budzinski, M Stuiver, ...
Journal of proteome research 19 (1), 327-336, 2019
92019
Analysis of free modeling predictions by RBO aleph in CASP 11
M Mabrouk, T Werner, M Schneider, I Putz, O Brock
Proteins: Structure, Function, and Bioinformatics 84, 87-104, 2016
92016
An integrated workflow for cross-linking/mass spectrometry. bioRxiv
ML Mendes, L Fischer, ZA Chen, M Barbon, FJ O’reilly, ...
Preprint, 2018
62018
GluonTS: Probabilistic Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
arXiv preprint arXiv:1906.05264, 2019
52019
GluonTS: Probabilistic and Neural Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
Journal of Machine Learning Research 21 (116), 1-6, 2020
42020
The system can't perform the operation now. Try again later.
Articles 1–20