| Accession |
PRJCA028278 |
| Title |
Perturbation Response Prediction |
| Relevance |
Medical |
| Data types |
Transcriptome or Gene expression
Single cell sequencing
|
| Organisms |
Homo
|
| Description |
We present a perturbation-conditioned deep generative model named PRnet for predicting transcriptional responses to novel chemical perturbations that were never experimentally perturbed at bulk and single-cell levels. PRnet screened four compound libraries and generated a large-scale integration atlas of perturbation profiles, including 1) 82 cell lines perturbed by 935 FDA-approved drugs, 2) 88 cell lines perturbed by 4,158 active compounds, 3) 14 CRC cell lines perturbed by 30,456 natural compounds, 4) 6 SCLC cell lines perturbed by 29,670 drug-like compounds and 5) 54 tissues perturbed by 935 FDA-approved drugs. |
| Sample scope |
Multiisolate |
| Release date |
2024-07-19 |
| Publication |
| PubMed ID |
Article title |
Journal name |
DOI |
Year |
| 39462106
|
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
|
Nature Communications
|
10.1038/s41467-024-53457-1
|
2024
|
|
|
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| Chinese Academy of Sciences (CAS)
|
|
N/A
|
|
|
|
| External link |
|
| Submitter |
Xiaoning
Qi (qixiaoning19s@ict.ac.cn)
|
| Organization |
Institute of Computing Technology, Chinese Academy of Sciences |
| Submission date |
2024-07-19 |
