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.
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Item Type: | Article |
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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: | 19 Jan 2023 00:00 |
DOI: | 10.1109/ACCESS.2020.3002923 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3076792 |
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- SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity. (deposited 02 Mar 2020 11:27) [Currently Displayed]
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