Discovering epistasis interactions in Alzheimer?s disease using integrated framework of ensemble learning and multifactor dimensionality reduction (MDR)

Alzheimer's disease (AD) is a complex disorder with strong genetic factors. The proposed framework is applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We present a novel framework integrating ensemble learning and MDR constructive induction algorithm to discover epistasis interac-tions associated with AD in a computationally efficient method. Discovering epistasis interactions is a big challenge and significantly impacts personalized medicine (PM). The applied ensemble learning algo-rithms are random forests (RF) with Gini index and permutation importance, Extreme Gradient Boosting (XGBoost), and classification and regression trees (CART). The classification accuracy of 5-way models varied between (0.8674-0.8758), whereas the accuracy of 2-way, 3-way, and 4-way models varied between (0.6515-0.6649), (0.7071-0.7170), and (0.7811-0.7878) respectively. The promising results of this proposed framework show high-ranked risk genes and up to 5-way epistasis models that contribute to the disease risk efficiently and at higher accuracy.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).