Montenaz, C, Fergus, P, Chalmers, C, Malim, N, Abdulaimma, B, Reilly, D and falciani, F (2020) SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity. IEEE Access. ISSN 2169-3536

Abdulaimma, B (2019) High Dimensional Analysis of Genetic Data for the Classification of Type 2 Diabetes Using Advanced Machine Learning Algorithms. Doctoral thesis, Liverpool John Moores University.

Fergus, P, Montanez, C, Abdulaimma, B, Lisboa, P, Chalmers, C and Pineless, B (2018) Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. IEEE/ACM Transactions on Computational Biology and Bioinformatics. ISSN 1545-5963

Aday Curbelo Montañez, C, Fergus, P, Hussain, A, Al-Jumeily, D, Abdulaimma, B and Al-askar, H (2016) A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweigh. In: Intelligent Computing Theories and Application: Lecture Notes in Computer Science , 9771. pp. 808-819. (2016 International Conference on Intelligent Computation, 02 August 2016 - 05 August 2016, Lanzhou,China).

Hussain, A, Abdulaimma, B, Fergus, P, Al-Jumeily, D, Aday Curbelo Montañez, C, Hind, J and Radi, N Association Mapping Approach into Type 2 Diabetes using Biomarkers and Clinical Data. In: International Conference on Intelligent Computing, 07 August 2017 - 10 August 2017, Liverpool, UK. (Accepted)

Abdulaimma, B, Hussain, A, Fergus, P, Al-Jumeily, D, Lisboa, P, Huang, D-S and Radi, N Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors. In: 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), 08 July 2018 - 13 April 2018, Brazil. (Accepted)