| Validity of machine learning in biology and medicine increased through collaborations across fields of expertise M Littmann, K Selig, L Cohen-Lavi, Y Frank, P Hönigschmid, E Kataka, ... Nature Machine Intelligence 2 (1), 18-24, 2020 | 57 | 2020 |
| Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors A Mösch, S Raffegerst, M Weis, DJ Schendel, D Frishman Frontiers in Genetics 10, 1141, 2019 | 44 | 2019 |
| Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues V Jaravine, A Mösch, S Raffegerst, DJ Schendel, D Frishman BMC cancer 17, 1-9, 2017 | 26 | 2017 |
| Differential expression analysis of human endogenous retroviruses based on ENCODE RNA-seq data K Haase, A Mösch, D Frishman BMC medical genomics 8, 1-12, 2015 | 25 | 2015 |
| On TCR binding predictors failing to generalize to unseen peptides F Grazioli, A Mösch, P Machart, K Li, I Alqassem, TJ O’Donnell, MR Min Frontiers in Immunology 13, 1014256, 2022 | 21 | 2022 |
| Attentive Variational Information Bottleneck for TCR–peptide interaction prediction F Grazioli, P Machart, A Mösch, K Li, LV Castorina, N Pfeifer, MR Min Bioinformatics 39 (1), btac820, 2023 | 3 | 2023 |
| TCRpair: prediction of functional pairing between HLA-A* 02: 01-restricted T-cell receptor α and β chains A Mösch, D Frishman Bioinformatics 37 (21), 3938-3940, 2021 | 1 | 2021 |
| MAGEA1 Specific T Cell Receptors and Their Use C Wehner, M Weis, S Raffegerst, A Mösch US Patent App. 17/600,518, 2023 | | 2023 |
| Computational methods to improve development of T cell based cancer immunotherapies AF Mösch Technische Universität München, 2022 | | 2022 |
