| Wasserstein weisfeiler-lehman graph kernels M Togninalli, E Ghisu, F Llinares-López, B Rieck, K Borgwardt Advances in neural information processing systems 32, 2019 | 164 | 2019 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ... Nature biotechnology 33 (9), 933-940, 2015 | 110 | 2015 |
| Efficient and modular implicit differentiation M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-López, ... Advances in neural information processing systems 35, 5230-5242, 2022 | 93 | 2022 |
| Graph kernels: State-of-the-art and future challenges K Borgwardt, E Ghisu, F Llinares-López, L O’Bray, B Rieck Foundations and Trends® in Machine Learning 13 (5-6), 531-712, 2020 | 77 | 2020 |
| 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 | 71 | 2015 |
| graphkernels: R and Python packages for graph comparison M Sugiyama, ME Ghisu, F Llinares-López, K Borgwardt Bioinformatics 34 (3), 530-532, 2018 | 63 | 2018 |
| Significant subgraph mining with multiple testing correction M Sugiyama, FL López, N Kasenburg, KM Borgwardt Proceedings of the 2015 SIAM International Conference on Data Mining, 37-45, 2015 | 51* | 2015 |
| Genome-wide detection of intervals of genetic heterogeneity associated with complex traits F Llinares-López, DG Grimm, DA Bodenham, U Gieraths, M Sugiyama, ... Bioinformatics 31 (12), i240-i249, 2015 | 41 | 2015 |
| 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 29, 2016 | 37 | 2016 |
| Genome-wide genetic heterogeneity discovery with categorical covariates F Llinares-López, L Papaxanthos, D Bodenham, D Roqueiro, ... Bioinformatics 33 (12), 1820-1828, 2017 | 19 | 2017 |
| Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma Y Liu, M Brossard, C Sarnowski, A Vaysse, M Moffatt, ... Scientific reports 7 (1), 1-10, 2017 | 17 | 2017 |
| DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer G Baid, DE Cook, K Shafin, T Yun, F Llinares-López, Q Berthet, ... Nature Biotechnology 41 (2), 232-238, 2023 | 15 | 2023 |
| 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 | 12 | 2019 |
| Deep embedding and alignment of protein sequences F Llinares-López, Q Berthet, M Blondel, O Teboul, JP Vert Nature Methods 20 (1), 104-111, 2023 | 11 | 2023 |
| Deepconsensus: Gap-aware sequence transformers for sequence correction G Baid, DE Cook, K Shafin, T Yun, F Llinares-Lopez, Q Berthet, ... BioRxiv, 2021.08. 31.458403, 2021 | 8 | 2021 |
| Machine learning for biomarker discovery: significant pattern mining F Llinares-Lopez, K Borgwardt Analyzing Network Data in Biology and Medicine: An Interdisciplinary …, 2019 | 5 | 2019 |
| Efficient and modular implicit differentiation, 2021 M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-Lopez, ... arXiv preprint arXiv:2105.15183, 0 | 5 | |
| Significant Pattern Mining for Biomarker Discovery F Llinares-López ETH Zurich, 2018 | 3 | 2018 |
| Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning C Weis, A Cuénod, B Rieck, F Llinares-López, O Dubuis, S Graf, C Lang, ... bioRxiv 1, 1-35, 2020 | 2 | 2020 |
| Learning Energy Networks with Generalized Fenchel-Young Losses M Blondel, F Llinares-López, R Dadashi, L Hussenot, M Geist arXiv preprint arXiv:2205.09589, 2022 | 1 | 2022 |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
