A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweight. - C. Aday Curbelo Montañez; Paul Fergus; Abir Jaafar Hussain; Dhiya Al-Jumeily; B. Abdulaimma; Haya Al-Askar (ICIC (1), 2016)

Association Mapping Approach into Type 2 Diabetes Using Biomarkers and Clinical Data. - Basma Abdulaimma; Abir Hussain; Paul Fergus; Dhiya Al-Jumeily; Casimiro Aday Curbelo Montañez; Jade Hind (ICIC (2), 2017)

A robust method for the interpretation of genomic data. - Jade Hind; Abir Jaafar Hussain; Dhiya Al-Jumeily; Basma Abdulaimma; Casimiro Aday Curbelo Montañez; Paulo J. G. Lisboa (IJCNN, 2017)

Machine learning approaches for the prediction of obesity using publicly available genetic profiles. - Casimiro Aday Curbelo Montañez; Paul Fergus; Abir Hussain; Dhiya Al-Jumeily; Basma Abdulaimma; Jade Hind; Naeem Radi (IJCNN, 2017)

Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors. - Basma Abdulaimma; Abir Hussain; Paul Fergus; Dhiya Al-Jumeily; Paulo Lisboa; De-Shuang Huang; Naeem Radi (CEC, 2018)

Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. - Paul Fergus; Casimiro Aday Curbelo Montañez; Basma Abdulaimma; Paulo Lisboa; Carl Chalmers (CoRR, 2018)

Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder. - Basma Abdulaimma; Paul Fergus; Carl Chalmers (CoRR, 2018)

High dimensional analysis of genetic data for the classification of type 2 diabetes using advanced machine learning algorithms. - B. Abdulaimma (2019)

Deep Learning and Genome-Wide Association Studies for the Classification of Type 2 Diabetes. - Basma Abdulaimma; Paul Fergus; Carl Chalmers; Casimiro Aday Curbelo Montañez (IJCNN, 2020)

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; Nurul Hashimah Ahamed Hassain Malim; Basma Abdulaimma; Denis Reilly 001; Francesco Falciani (IEEE Access, 2020)

Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. - Paul Fergus; Casimiro Aday Curbelo Montañez; Basma Abdulaimma; Paulo Lisboa; Carl Chalmers; Beth Pineles (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)