Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Open Access
- 31 March 2026
- journal article
- Published by Elsevier in Cell Systems
- Vol. 17 (5) , 101570
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Genentech
This publication has 42 references indexed in Scilit:
- Clarifying the causes of consistent and inconsistent findings in geneticsGenetic Epidemiology, 2022
- S100A8/A9 in COVID-19 pathogenesis: Impact on clinical outcomesCytokine & Growth Factor Reviews, 2021
- An interactive single cell web portal identifies gene and cell networks in COVID-19 host responsesiScience, 2021
- Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell CompartmentCell, 2020
- Discovery of rare cells from voluminous single cell expression dataNature Communications, 2018
- From genome-wide associations to candidate causal variants by statistical fine-mappingNature Reviews Genetics, 2018
- Reconstructing and Reprogramming the Tumor-Propagating Potential of Glioblastoma Stem-like CellsCell, 2014
- The big challenges of big dataNature, 2013
- Tackling the widespread and critical impact of batch effects in high-throughput dataNature Reviews Genetics, 2010
- Gene Ontology: tool for the unification of biologyNature Genetics, 2000