DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment

Multi-domain translation between single-cell imaging and sequencing data using autoencoders

(2021) Karren Dai Yang et al.   Nature Communications

Single-cell RNA-seq denoising using a deep count autoencoder

(2019) Gökcen Eraslan et al.   Nature Communications

Dr.VAE: Improving drug response prediction via modeling of drug perturbation effects

(2019) Ladislav Rampášek et al.   BIOINFORMATICS

scGen predicts single-cell perturbation responses

(2019) Mohammad Lotfollahi et al.   NATURE METHODS

Exploring single-cell data with deep multitasking neural networks

(2019) Matthew Amodio et al.   NATURE METHODS

A framework for identification of on- and off-target transcriptional responses to drug treatment

(2019) Yi Huang et al.   Scientific Reports

The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel

(2018) Anne Monks et al.   CANCER RESEARCH

PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools

(2018) Huaiyu Mi et al.   NUCLEIC ACIDS RESEARCH

DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery

(2018) Chaoyang Ye et al.   Nature Communications

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

(2018) Divyanshu Talwar et al.   Scientific Reports

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

(2017) Mario Niepel et al.   Nature Communications

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

(2017) Jie Tan et al.   Cell Systems

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

(2016) Lujia Chen et al.   BMC BIOINFORMATICS

Deep Dictionary Learning

(2016) Snigdha Tariyal et al.   IEEE Access

Transcriptional data: a new gateway to drug repositioning?

(2012) Francesco Iorio et al.   DRUG DISCOVERY TODAY

Discovery of drug mode of action and drug repositioning from transcriptional responses

(2010) F. Iorio et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA