Work

A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Year: 2021

Type: article

Source: Nature Protocols

DOI: 10.1038/s41596-020-00432-x

Authors: Jude M. Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, Pei-Hsun Wu

Institutions: Johns Hopkins University, Johns Hopkins Medicine, Sidney Kimmel Comprehensive Cancer Center

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: U.S. Department of Health and Human Services, National Institutes of Health, National Institute on Aging, National Cancer Institute

Awards: G1373639953, G1912770925, Single-cell phenotyping for therapeutic stratification in pancreatic cancer, G2909399862, G601512365, The Johns Hopkins Physical Sciences Oncology Center, G7079668014, Validation of Nuclear Morphology as a Biomarker of Aging and Aging-Related Phenotypes, Older Americans Independence Center, Center for Cancer Physics

OpenAlex ID: W3120345372

FWCI: 7.566

Cites: 76

Cited by: 118

Related to: 10