Publications

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.

PDF Code DOI

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.

PDF Code

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

Query to reference single-cell integration with 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.

PDF Code DOI

Query to reference single-cell integration with transfer learning

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.

PDF Code

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.

PDF Code DOI

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.

PDF

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