Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

Abstract

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

A review and analysis of key biomarkers in Alzheimer’s disease

“…Researchers are also dedicated to using artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms for detecting AD and integrating different types of data ( Alamro et al, 2023 ). These data types include, but are not limited to, neuroimaging data, non-coding RNAs, transcriptomic data ( Qorri et al, 2020 ), miRNA biomarkers ( Xu et al, 2022 ), or other genomic data ( Monk et al, 2021 ).…”

Screening Techniques for Drug Discovery in Alzheimer’s Disease

“…In the past decade, various drug screening techniques such as high throughput screening (HTS) and the affordability of omics data sources − have driven much research in this area. Drug screening technology allows the use of automation to rapidly test thousands of drug-like molecules from existing biomedical platforms against many putative targets.…”

Predicting dementia progression with fully connected cascade neural networks

“…Machine learning algorithms have shown high potential for analysing complex medical data for clinical decisionmaking 25 . In particular, fully connected cascade (FCC) neural networks have been utilized in several studies to analyse disease markers 26,27 .…”