19 citations · 60 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers
Paul Fergus, Carl Chalmers, David Tully
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pr…
Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
Casimiro A. Curbelo Montañez, Paul Fergus, Carl Chalmers +1
The aetiology of polygenic obesity is multifactorial, which indicates that life-style and environmental factors may influence multiples genes to aggravate this disorder. Several lo…
Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs
Casimiro Adays Curbelo Montañez, Paul Fergus, Almudena Curbelo Montañez +1
In this paper, association results from genome-wide association studies (GWAS) are combined with a deep learning framework to test the predictive capacity of statistically signific…
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…