| Fast and memory-efficient significant pattern mining via permutation testing F Llinares-López, M Sugiyama, L Papaxanthos, K Borgwardt Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 66 | 2015 |
| Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings M Le, S Roller, L Papaxanthos, D Kiela, M Nickel arXiv preprint arXiv:1902.00913, 2019 | 65 | 2019 |
| Finding significant combinations of features in the presence of categorical covariates L Papaxanthos, F Llinares-López, D Bodenham, K Borgwardt Advances in Neural Information Processing Systems, 2279-2287, 2016 | 35 | 2016 |
| JKOnet: Proximal Optimal Transport Modeling of Population Dynamics C Bunne, L Meng-Papaxanthos, A Krause, M Cuturi arXiv preprint arXiv:2106.06345, 2021 | 33* | 2021 |
| Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping S Höllerer, L Papaxanthos, AC Gumpinger, K Fischer, C Beisel, ... Nature communications 11 (1), 1-15, 2020 | 32 | 2020 |
| Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein M Cuturi, L Meng-Papaxanthos, Y Tian, C Bunne, G Davis, O Teboul arXiv preprint arXiv:2201.12324, 2022 | 18 | 2022 |
| Genome-wide genetic heterogeneity discovery with categorical covariates F Llinares-López, L Papaxanthos, D Bodenham, D Roqueiro, ... Bioinformatics 33 (12), 1820-1828, 2017 | 18 | 2017 |
| Machine learning for single cell genomics data analysis F Raimundo, L Papaxanthos, C Vallot, JP Vert Current Opinion in Systems Biology, 2021 | 16 | 2021 |
| CASMAP: detection of statistically significant combinations of SNPs in association mapping F Llinares-López, L Papaxanthos, D Roqueiro, D Bodenham, K Borgwardt Bioinformatics 35 (15), 2680-2682, 2019 | 11 | 2019 |
| Semi-supervised single-cell cross-modality translation using Polarbear R Zhang, L Meng-Papaxanthos, JP Vert, WS Noble bioRxiv, 2021 | 7 | 2021 |
| Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions T Kucera, M Togninalli, L Meng-Papaxanthos bioRxiv, 2021 | 2 | 2021 |
| networkGWAS: A network-based approach for genome-wide association studies in structured populations G Muzio, L O’Bray, L Meng-Papaxanthos, J Klatt, K Borgwardt bioRxiv, 2021 | | 2021 |
| Machine Learning for Interaction Discovery in Genetics and Bioengineering L Papaxanthos ETH Zurich, 2020 | | 2020 |
| Machine Learning Annex to: Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping S Höllerer, L Papaxanthos, AC Gumpinger, K Fischer, C Beisel, ... | | |
| Supplementary Material for Finding significant combinations of features in the presence of categorical covariates L Papaxanthos, F Llinares-López, D Bodenham, K Borgwardt | | |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
