Publications

Multigrate: single-cell multi-omic data integration

Multigrate uses variational inference and multimodal learning to integrate multi-modal single-cell datasets.
Lotfollahi et al.
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Multigrate: single-cell multi-omic data integration

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. …
Lotfollahi et al.
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Mapping single-cell data to reference atlases by transfer learning

Jointly learning T-cell receptor and transcriptomic information to decipher the immune response

mvTCR uses variational inference and multimodal learning to integrate T-cell receptor sequences with gene expression.
Yang et al.
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Jointly learning T-cell receptor and transcriptomic information to decipher the immune response

Compositional perturbation autoencoder for single-cell response modeling

Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high-throughput study of drug and …
Lotfollahi et al.
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Compositional perturbation autoencoder for single-cell response modeling

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation …
Lotfollahi et al.
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Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Conditional out-of-sample generation for unpaired data using trVAE

While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors …
Lotfollahi et al.
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Conditional out-of-sample generation for unpaired data using trVAE

scGen predicts single-cell perturbation responses

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based …
Lotfollahi et al.
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scGen predicts single-cell perturbation responses

Deep packet: a novel approach for encrypted traffic classification using deep learning

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies …
Lotfollahi et al.
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Deep packet: a novel approach for encrypted traffic classification using deep learning