| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2209 | 2023 |
| Deep learning for computational biology C Angermueller, T Pärnamaa, L Parts, O Stegle Molecular systems biology 12 (7), 878, 2016 | 1650 | 2016 |
| 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 | 1186 | 2014 |
| 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 | 932 | 2016 |
| 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 | 756 | 2016 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 699 | 2024 |
| DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning C Angermueller, HJ Lee, W Reik, O Stegle Genome biology 18, 1-13, 2017 | 595 | 2017 |
| 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 | 241 | 2011 |
| 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 | 181 | 2018 |
| 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 | 170 | 2014 |
| MODEL-BASED REINFORCEMENT LEARNING FOR BIO-LOGICAL SEQUENCE DESIGN C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ... ICML2020, 2019 | 141 | 2019 |
| 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 | 87 | 2018 |
| Discriminative modelling of context-specific amino acid substitution probabilities C Angermüller, A Biegert, J Söding Bioinformatics 28 (24), 3240-3247, 2012 | 63 | 2012 |
| 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 | 61 | 2020 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688 10, 2016 | 51 | 2016 |
| 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 | 30 | 2021 |
| Apollo: Transferable architecture exploration A Yazdanbakhsh, C Angermueller, B Akin, Y Zhou, A Jones, M Hashemi, ... arXiv preprint arXiv:2102.01723, 2021 | 27 | 2021 |
| 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 (1), 398968, 2013 | 24 | 2013 |
| Developing deep learning applications for life science and pharma industry D Siegismund, V Tolkachev, S Heyse, B Sick, O Duerr, S Steigele Drug research 68 (06), 305-310, 2018 | 14 | 2018 |
| Biological Sequence Design using Batched Bayesian Optimization D Belanger, S Vora, Z Mariet, R Deshpande, D Dohan, C Angermueller, ... NeurIPS2019 Workshop, 2019 | 13 | 2019 |
Oliver StegleGerman Cancer Research Center (DKFZ) & EMBL HeidelbergVerified email at dkfz.de
