Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 3 - Allied Health Professions, Dentistry, Nursing and Pharmacy
- Output identifier
- 34 - 1349951
- Type
- D - Journal article
- DOI
-
10.7554/elife.50936
- Title of journal
- eLife
- Article number
- e50936
- First page
- -
- Volume
- 9
- Issue
- -
- ISSN
- 2050084X
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
7
- Research group(s)
-
A - Centre for Health Ageing and Understanding Disease
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
