3 citations · 3 across the 1 of their papers we have counts for
3 papers
SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity
Casimiro Aday Curbelo Montañez, Paul Fergus, Carl Chalmers +4
One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Sin…
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder
Basma Abdulaimma, Paul Fergus, Carl Chalmers
2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactor…
Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women
Paul Fergus, Casimiro Curbelo Montanez, Basma Abdulaimma +2
Genome Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. GWAS typically use a p-value threshold of 5 x 10-8 t…