Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data

Abstract

Alzheimer's disease (AD) is the most uncertain form of Dementia in terms of finding out the mechanism. AD does not have a vital genetic factor to relate to. There were no reliable techniques and methods to identify the genetic risk factors associated with AD in the past. Most of the data available were from the brain images. However, recently, there have been drastic advancements in the high-throughput techniques in bioinformatics. It has led to focused researches in discovering the AD causing genetic risk factors. Recent analysis has resulted in considerable prefrontal cortex data with which classification and prediction models can be developed for AD. We have developed a Deep Belief Network-based prediction model using the DNA Methylation and Gene Expression Microarray Data, with High Dimension Low Sample Size (HDLSS) issues. To overcome the HDLSS challenge, we performed a two-layer feature selection considering the biological aspects of the features as well. In the two-layered feature selection approach, first the differentially expressed genes and differentially methylated positions are identified, then both the datasets are combined using Jaccard similarity measure. As the second step, an ensemble-based feature selection approach is implemented to further narrow down the gene selection. The results show that the proposed feature selection technique outperforms the existing commonly used feature selection techniques, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Correlation-based Feature Selection (CBS). Furthermore, the Deep Belief Network-based prediction model performs better than the widely used Machine Learning models. Also, the multi-omics dataset shows promising results compared to the single omics.