| Wasserstein Weisfeiler-Lehman Graph Kernels M Togninalli, E Ghisu, F Llinares-López, B Rieck, K Borgwardt Advances in Neural Information Processing Systems 32, 6436--6446, 2019 | 277 | 2019 |
| Neural persistence: A complexity measure for deep neural networks using algebraic topology B Rieck, M Togninalli, C Bock, M Moor, M Horn, T Gumbsch, K Borgwardt International Conference on Learning Representation (ICLR) 2019, 2019 | 157 | 2019 |
| The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog M Togninalli, Ü Seren, D Meng, J Fitz, M Nordborg, D Weigel, ... Nucleic acids research 46 (D1), D1150-D1156, 2018 | 99 | 2018 |
| AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana M Togninalli, Ü Seren, JA Freudenthal, JG Monroe, D Meng, M Nordborg, ... Nucleic acids research 48 (D1), D1063-D1068, 2020 | 68 | 2020 |
| Sequence-dependent mechanical, photophysical and electrical properties of pi-conjugated peptide hydrogelators HAM Ardoña, K Besar, M Togninalli, HE Katz, JD Tovar Journal of Materials Chemistry C 3 (25), 6505-6514, 2015 | 58 | 2015 |
| RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection K Avican, J Aldahdooh, M Togninalli, AKMF Mahmud, J Tang, ... Nature communications 12 (1), 3282, 2021 | 53 | 2021 |
| Conditional generative modeling for de novo protein design with hierarchical functions T Kucera, M Togninalli, L Meng-Papaxanthos Bioinformatics 38 (13), 3454-3461, 2022 | 29 | 2022 |
| Pretransplant kinetics of anti-HLA antibodies in patients on the waiting list for kidney transplantation M Togninalli, D Yoneoka, AGA Kolios, K Borgwardt, J Nilsson Journal of the American Society of Nephrology 30 (11), 2262-2274, 2019 | 14 | 2019 |
| A wasserstein subsequence kernel for time series C Bock, M Togninalli, E Ghisu, T Gumbsch, B Rieck, K Borgwardt 2019 IEEE International Conference on Data Mining (ICDM), 964-969, 2019 | 13 | 2019 |
| Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics M Togninalli, X Wang, T Kucera, S Shrestha, P Juliana, S Mondal, F Pinto, ... Bioinformatics 39 (6), btad336, 2023 | 12 | 2023 |
| Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts M Togninalli, D Roqueiro, COPDGene Investigators, KM Borgwardt Bioinformatics 34 (17), i687-i696, 2018 | 9 | 2018 |
| Reference Panels for ARDISS: Reference Panels DS Roqueiro, M Togninalli ETH Zurich, 2023 | | 2023 |
| Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics M Togninalli, X Wang, T Kucera, S Shrestha, P Juliana, S Mondal, F Pinto, ... Dryad, 2021 | | 2021 |
| Exploring the Genotype-Phenotype Relationship Using Big Data and Machine Learning M Togninalli ETH Zurich, 2020 | | 2020 |
| PRE-TRANSPLANT KINETICS OF ANTI-HLA ANTIBODIES IN PATIENTS ON THE KIDNEY TRANSPLANT WAITING LIST M Togninalli, D Yoneoka, AGA Kolios, K Borgwardt, J Nilsson HLA 93 (5), 262-262, 2019 | | 2019 |
| Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design T Kucera, KM Borgwardt, M Togninalli, L Papaxanthos | | |
| Sequence-dependent optoelectronic and mechanical properties of π-conjugated peptide hydrogelators HAM Ardoña, K Besar, M Togninalli, HE Katz, JD Tovar | | |
| Fast Imputation of Summary Statistics Based on Local LD Structure M Togninalli, DS Roquerio, KM Borgwardt | | |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryGeverifieerd e-mailadres voor biochem.mpg.de