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Christof Angermueller
Christof Angermueller
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Title
Cited by
Cited by
Year
Deep learning for computational biology
C Angermueller, T Pärnamaa, L Parts, O Stegle
Molecular systems biology 12 (7), 878, 2016
12292016
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
SA Smallwood, HJ Lee, C Angermueller, F Krueger, H Saadeh, J Peat, ...
Nature methods 11 (8), 817-820, 2014
9322014
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
8572016
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
C Angermueller, SJ Clark, HJ Lee, IC Macaulay, MJ Teng, TX Hu, ...
Nature methods 13 (3), 229-232, 2016
5812016
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
C Angermueller, HJ Lee, W Reik, O Stegle
Genome biology 18 (1), 1-13, 2017
4502017
The Mre11: Rad50 structure shows an ATP-dependent molecular clamp in DNA double-strand break repair
K Lammens, DJ Bemeleit, C Möckel, E Clausing, A Schele, S Hartung, ...
Cell 145 (1), 54-66, 2011
2142011
Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes
C Winkler, J Krumsiek, F Buettner, C Angermüller, EZ Giannopoulou, ...
Diabetologia 57, 2521-2529, 2014
1352014
Deep learning for predicting refractive error from retinal fundus images
AV Varadarajan, R Poplin, K Blumer, C Angermueller, J Ledsam, ...
Investigative ophthalmology & visual science 59 (7), 2861-2868, 2018
1182018
MODEL-BASED REINFORCEMENT LEARNING FOR BIO-LOGICAL SEQUENCE DESIGN
C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ...
ICML2020, 2019
732019
Genome-scale oscillations in DNA methylation during exit from pluripotency
S Rulands, HJ Lee, SJ Clark, C Angermueller, SA Smallwood, F Krueger, ...
Cell systems 7 (1), 63-76. e12, 2018
722018
Discriminative modelling of context-specific amino acid substitution probabilities
C Angermüller, A Biegert, J Söding
Bioinformatics 28 (24), 3240-3247, 2012
572012
Population-based black-box optimization for biological sequence design
C Angermueller, D Belanger, A Gane, Z Mariet, D Dohan, K Murphy, ...
International Conference on Machine Learning, 324-334, 2020
342020
Cloud prediction of protein structure and function with PredictProtein for Debian
L Kaján, G Yachdav, E Vicedo, M Steinegger, M Mirdita, C Angermüller, ...
BioMed Research International 2013, 2013
232013
Apollo: Transferable architecture exploration
A Yazdanbakhsh, C Angermueller, B Akin, Y Zhou, A Jones, M Hashemi, ...
arXiv preprint arXiv:2102.01723, 2021
112021
A flexible approach to autotuning multi-pass machine learning compilers
PM Phothilimthana, A Sabne, N Sarda, KS Murthy, Y Zhou, ...
2021 30th International Conference on Parallel Architectures and Compilation …, 2021
102021
Biological Sequence Design using Batched Bayesian Optimization
D Belanger, S Vora, Z Mariet, R Deshpande, D Dohan, C Angermueller, ...
NeurIPS2019 Workshop, 2019
42019
Apollo: Transferable architecture exploration
A Jones, A Yazdanbakhsh, B Akin, C Angermueller, JP Laudon, ...
32020
Agreement between saliency maps and human-labeled regions of interest: applications to skin disease classification
N Singh, K Lee, D Coz, C Angermueller, S Huang, A Loh, Y Liu
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
32020
A comparison of generative models for sequence design
A Gane, D Belanger, D Dohan, C Angermueller, R Deshpande, S Vora, ...
32019
Deep Uncertainty and the Search for Proteins
Z Mariet, G Jerfel, Z Wang, C Angermüller, D Belanger, S Vora, M Bileschi, ...
Workshop: Machine Learning for Molecules, 2020
22020
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Articles 1–20