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Laetitia Meng-Papaxanthos
Laetitia Meng-Papaxanthos
Google DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
822015
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
742019
Proximal optimal transport modeling of population dynamics
C Bunne, L Papaxanthos, A Krause, M Cuturi
International Conference on Artificial Intelligence and Statistics, 6511-6528, 2022
712022
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
582022
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), 3551, 2020
532020
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
392016
Machine learning for single-cell genomics data analysis
F Raimundo, L Meng-Papaxanthos, C Vallot, JP Vert
Current Opinion in Systems Biology 26, 64-71, 2021
242021
Genome-wide genetic heterogeneity discovery with categorical covariates
F Llinares-López, L Papaxanthos, D Bodenham, D Roqueiro, ...
Bioinformatics 33 (12), 1820-1828, 2017
242017
Mapping cells through time and space with moscot
D Klein, G Palla, M Lange, M Klein, Z Piran, M Gander, ...
bioRxiv, 2023.05. 11.540374, 2023
192023
Conditional generative modeling for de novo protein design with hierarchical functions
T Kucera, M Togninalli, L Meng-Papaxanthos
Bioinformatics 38 (13), 3454-3461, 2022
192022
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
142019
Semi-supervised single-cell cross-modality translation using Polarbear
R Zhang, L Meng-Papaxanthos, JP Vert, WS Noble
International Conference on Research in Computational Molecular Biology, 20-35, 2022
132022
Protnlm: Model-based natural language protein annotation
A Gane, ML Bileschi, D Dohan, E Speretta, A Héliou, ...
Preprint, 2022
132022
networkGWAS: a network-based approach to discover genetic associations
G Muzio, L O’Bray, L Meng-Papaxanthos, J Klatt, K Fischer, K Borgwardt
Bioinformatics 39 (6), btad370, 2023
32023
Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning
R Zhang, L Meng-Papaxanthos, J Vert, WS Noble
Journal of Computational Biology 29 (11), 1198-1212, 2022
22022
LSMMD-MA: scaling multimodal data integration for single-cell genomics data analysis
L Meng-Papaxanthos, R Zhang, G Li, M Cuturi, WS Noble, JP Vert
Bioinformatics 39 (7), btad420, 2023
12023
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
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