Design of a flexible, user friendly feature matrix generation system and its application on biomedical datasets
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 089-221846-5308
- Type
- D - Journal article
- DOI
-
10.1007/s10723-020-09518-y
- Title of journal
- Journal Of Grid Computing
- Article number
- -
- First page
- 507
- Volume
- 18
- Issue
- 3
- ISSN
- 1570-7873
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
https://link-springer-com.ezproxy.brunel.ac.uk/content/pdf/10.1007/s10723-020-09518-y.pdf
- 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
- No
- Number of additional authors
-
0
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a journal ranked in the top third in Computer Science: Theory and Methods according to the Web of Science (2019), this work proposes a user friendly system to generate feature matrices in a way that is flexible, scalable and extendable by making use of The Berkeley Open Infrastructure for Network Computing (BOINC) software. This is significant as the majority of bioscience researchers are not computing experts able to analyse their data using command line programming.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
