Author

David López-Paz

Meta (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Anna Klimovskaia· 2 shared Carlo De DonnoiD· 1 shared F. Alexander WolfiD· 1 shared Fabian Joachim TheisiD· 1 shared François ChartoniD· 1 shared Ignacio L. IbarraiD· 1 shared Jean-Rémi KingiD· 1 shared Léon BottouiD· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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2 shared papersBioinformatics

Anna Klimovskaia

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Learning interpretable cellular responses tocomplex perturbations in high-throughput screens

Mohammad Lotfollahi, Anna Klimovskaia, Carlo De Donno, Yuge Ji, Ignacio L. Ibarra, F. Alexander Wolf, Nafissa Yakubova, Fabian Joachim Theis, David López-Paz

The compositional perturbation autoencoder separates drug, dose and cell-type effects in single-cell transcriptomes, predicts unseen combinations, and generates 5,329 unmeasured genetic combinations in silico.

bioRxiv · Apr 14, 2021 · PreprintBioinformatics

Poincare Maps for Analyzing Complex Hierarchies in Single-Cell Data

Anna Klimovskaia, David López-Paz, Léon Bottou, Maximilian Nickel

This paper introduces Poincare maps, a hyperbolic geometry-based method for analyzing complex hierarchies in single-cell data, achieving state-of-the-art results in visualization, clustering, and lineage detection.

Nature Communications · Jun 11, 2020Peer-reviewedBioinformatics

Discriminating the Influence of Correlated Factors from Multivariate Observations: the Back-to-Back Regression

Jean-Rémi King, François Charton, David López-Paz, Maxime Oquab

The paper introduces Back-to-Back regression to disentangle correlated factors from multivariate observations, showing it outperforms existing methods in causal identification.

bioRxiv · Mar 5, 2020 · PreprintNeuroscience