Laetitia Meng-Papaxanthos

moscot unifies optimal transport for temporal, spatial and spatiotemporal single-cell mapping, using multimodal cost spaces and low-rank solvers to scale to 1.7 million mouse embryo cells.

This study uses 1D-CNNs to predict PCR amplification bias from DNA sequences, identifying self-priming as a previously overlooked mechanism.

LSMMD-MA scales multimodal data integration for single-cell genomics to handle up to a million cells per modality, improving on existing methods by two orders of magnitude.

networkGWAS improves network-based GWAS by using mixed models and permutation schemes to detect genetic associations with better statistical control.

This paper introduces a conditional generative adversarial network for protein design based on hierarchical Gene Ontology labels and evaluates it against deep learning baselines.

Polarbear translates between single-cell modalities using semi-supervised learning, improving accuracy over existing methods and enabling cross-modality cell matching.

This review surveys recent machine learning approaches for analyzing single-cell omics data from 2019-2020, focusing on methods for dimensionality reduction, batch normalization, cell type classification, trajectory inference, and multimodal integration.

The paper introduces DNA-based phenotypic recording to generate large-scale sequence-function datasets and uses deep learning to predict gene regulatory element function with high accuracy.