| Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany C Fritz, E Dorigatti, D Rügamer Scientific Reports 12 (1), 3930, 2022 | 60 | 2022 |
| Artificial intelligence in early drug discovery enabling precision medicine F Boniolo, E Dorigatti, AJ Ohnmacht, D Saur, B Schubert, MP Menden Expert Opinion on Drug Discovery 16 (9), 991-1007, 2021 | 50 | 2021 |
| N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation M Fossati, E Dorigatti, C Giuliano Semantic Web 9 (4), 413-439, 2018 | 25 | 2018 |
| Joint epitope selection and spacer design for string-of-beads vaccines E Dorigatti, B Schubert Bioinformatics 36 (Supplement_2), i643-i650, 2020 | 10 | 2020 |
| Graph-theoretical formulation of the generalized epitope-based vaccine design problem E Dorigatti, B Schubert PLOS Computational Biology 16 (10), e1008237, 2020 | 9 | 2020 |
| Joint Debiased Representation Learning and Imbalanced Data Clustering M Rezaei, E Dorigatti, D Rügamer, B Bischl 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 55-62, 2022 | 5* | 2022 |
| Positive-unlabeled learning with uncertainty-aware pseudo-label selection E Dorigatti, J Goschenhofer, B Schubert, M Rezaei, B Bischl arXiv preprint arXiv:2201.13192, 2022 | 5 | 2022 |
| Approximately Bayes-optimal pseudo-label selection J Rodemann, J Goschenhofer, E Dorigatti, T Nagler, T Augustin Uncertainty in Artificial Intelligence, 1762-1773, 2023 | 4* | 2023 |
| Frequentist uncertainty quantification in semi-structured neural networks E Dorigatti, B Schubert, B Bischl, D Rügamer International Conference on Artificial Intelligence and Statistics, 1924-1941, 2023 | 3 | 2023 |
| Predicting t cell receptor functionality against mutant epitopes E Dorigatti, F Drost, A Straub, P Hilgendorf, KI Wagner, B Bischl, D Busch, ... bioRxiv, 2023.05. 10.540189, 2023 | 3 | 2023 |
| Improved proteasomal cleavage prediction with positive-unlabeled learning E Dorigatti, B Bischl, B Schubert arXiv preprint arXiv:2209.07527, 2022 | 3 | 2022 |
| Robust and efficient imbalanced positive-unlabeled learning with self-supervision E Dorigatti, J Schweisthal, B Bischl, M Rezaei arXiv preprint arXiv:2209.02459, 2022 | 2 | 2022 |
| Selective background Monte Carlo simulation at Belle II J Kahn, E Dorigatti, K Lieret, A Lindner, T Kuhr EPJ Web of Conferences 245, 02028, 2020 | 1 | 2020 |
| Neural Architecture Search for Genomic Sequence Data A Scheppach, HA Gündüz, E Dorigatti, PC Münch, AC McHardy, B Bischl, ... 2023 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2023 | | 2023 |
| Proteasomal cleavage prediction: state-of-the-art and future directions I Ziegler, B Ma, B Bischl, E Dorigatti, B Schubert bioRxiv, 2023.07. 17.549305, 2023 | | 2023 |
| What cleaves? Is proteasomal cleavage prediction reaching a ceiling? I Ziegler, B Ma, E Nie, B Bischl, D Rügamer, B Schubert, E Dorigatti arXiv preprint arXiv:2210.12991, 2022 | | 2022 |
| Joint Debiased Representation and Image Clustering Learning with Self-Supervision SF Zheng, JE Nam, E Dorigatti, B Bischl, S Azizi, M Rezaei arXiv preprint arXiv:2209.06941, 2022 | | 2022 |
| The lessons I learnt supervising master’s students for the first time E Dorigatti Nature, 2021 | | 2021 |
| Predicting the Momentum Flux-Profile Relationship from Macro Weather Parameters E Dorigatti | | 2018 |
| Proteasomal cleavage prediction: state-of-the-art and future directions IZBMB Bischl, E Dorigatti, B Schubert | | |
