Using deep learning to associate human genes with age-related diseases
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 19169
- Type
- D - Journal article
- DOI
-
10.1093/bioinformatics/btz887
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 2202
- Volume
- 36
- Issue
- 7
- ISSN
- 1367-4803
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
https://kar.kent.ac.uk/79932/
- 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
-
4
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel Deep Neural Network (DNN) algorithm and a new DNN-interpretation approach that automatically identifies interesting genes for biological analysis. The work is significant because the new algorithm achieved overall significantly higher predictive accuracy than a number of state-of-the-art alternatives with age-related disease datasets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
