Work

Isolating salient variations of interest in single-cell data with contrastiveVI

Year: 2023

Type: article

Source: Nature Methods

DOI: 10.1038/s41592-023-01955-3

Authors: Ethan Weinberger, Chris Lin, Su-In Lee

Institution: University of Washington

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Health and Human Services, National Institutes of Health, National Institute on Aging, National Institute of General Medical Sciences

Awards: CAREER: Learning the Chromatin Network from ChIP-Seq Data, G1786123482, G3246045422, Collaborative Research: ABI Innovation: Interpretable Machine Learning to Identify Molecular Markers for Complex Phenotypes, Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets, G653274256, Opening the Black Box of Machine Learning Models, G8555350935

OpenAlex ID: W4385638616

FWCI: 5.361

Cites: 74

Cited by: 63

Related to: 10