A Novel Approach to Detecting Epistasis using Random Sampling Regularisation
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 928
- Type
- D - Journal article
- DOI
-
10.1109/TCBB.2019.2948330
- Title of journal
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Article number
- -
- First page
- 1535
- Volume
- 17
- Issue
- 5
- ISSN
- 1545-5963
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2019
- 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
-
3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This multidisciplinary work incorporated bioinformatics, computer science and mathematical approaches to analyse genetic breast cancer data and the detection of epistasis using random sampling regularisation. This is different to other bioinformatics standard approaches widely used in genome-wide association studies (GWAS) to determine significant single nucleotide polymorphisms (SNPs). The work represents a unique theoretical approach to displaying the significant SNPs using mathematical interpretation, validated using extensive experiments. The work was presented as an invited talk at the AI in Health and Care workshop, March 2019, London, organised by the Royal Society and the Academy of Medical Sciences.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
