SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity



Montanez, Casimiro A Curbelo, Fergus, Paul, Chalmers, Carl, Malim, Nurul Hashimah Ahamed Hassain, Abdulaimma, Basma, Reilly, Denis and Falciani, Francesco
(2020) SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity. IEEE ACCESS, 8. 112379 - 112392.

WarningThere is a more recent version of this item available.
[img] Text
1908.10166v1.pdf - Submitted Version

Download (920kB) | Preview
Item Type: Article
Uncontrolled Keywords: Obesity, Bioinformatics, Genomics, Diseases, Artificial neural networks, Deep learning, Association rules, autoencoders, deep learning, epistasis, genome-wide association studies (GWAS), obesity
Depositing User: Symplectic Admin
Date Deposited: 02 Mar 2020 11:27
Last Modified: 11 Aug 2020 10:17
DOI: 10.1109/ACCESS.2020.3002923
Related URLs:
URI: http://livrepository.liverpool.ac.uk/id/eprint/3076792

Available Versions of this Item