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Laetitia Meng-Papaxanthos
Laetitia Meng-Papaxanthos
Google Research, Brain team
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
622015
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
572019
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
342016
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
282020
Genome-wide genetic heterogeneity discovery with categorical covariates
F Llinares-López, L Papaxanthos, D Bodenham, D Roqueiro, ...
Bioinformatics 33 (12), 1820-1828, 2017
182017
JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
C Bunne, L Meng-Papaxanthos, A Krause, M Cuturi
arXiv preprint arXiv:2106.06345, 2021
15*2021
Machine learning for single cell genomics data analysis
F Raimundo, L Papaxanthos, C Vallot, JP Vert
Current Opinion in Systems Biology, 2021
112021
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
102019
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
82022
Semi-supervised single-cell cross-modality translation using Polarbear
R Zhang, L Meng-Papaxanthos, JP Vert, WS Noble
bioRxiv, 2021
12021
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
Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions
T Kucera, M Togninalli, L Meng-Papaxanthos
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
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