Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 29
- Type
- D - Journal article
- DOI
-
10.1016/j.cmpb.2017.05.001
- Title of journal
- Computer Methods and Programs in Biomedicine
- Article number
- -
- First page
- 11
- Volume
- 146
- Issue
- -
- ISSN
- 0169-2607
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2017
- 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
-
2
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a journal ranked A* in ERA2010, Q1 by Scimago, the output's originality lies within a new method for automated analysis of colon cancer data, enhancing colon cancer classification accuracy. Subsequently, the research has influenced research in lung cancer within the Symbiosis Institute of Technology, Pune, India, as well as other cancer classification work in Chongqin University, China, and Universiti Teknologi Malaysia, Malaysia. Since publication the co-author MuradAl-Rajab has built on this research and obtained a Research, Innovation, and Impact Grant in 2020 from Abu Dhabi University, UAE. https://www.adek.gov.ae/Abu-Dhabi-Research-Awards.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -