A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction

Abstract

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction.Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction.Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression.Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

Oxidized MIF is an Alzheimer’s Disease drug target relaying external risk factors to tau pathology

“…) and calmodulin-like 3 (CALML3; (Chen et al, 2019)). Even though these results suggested that multiple proteins, or a multiprotein complex, could be ligands of active compound, we focused on MIF because of its known role in infection and neurodegenerative disorders (Petralia et al, 2020).…”

Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics

“…A total of 10 standard algorithms, including support vector machine (SVM), random forest (RF), extra tree, adaptive boosting (AdaBoost), gradient boosting, multilayer perceptron (MLP), K-nearest neighbors (KNN), logistic regression, linear discriminant analysis, and Gaussian Naive Bayes classifier (Gaussian NB), were used in this study. First, we divided the GSE131617 into two parts according to 70% training and 30% testing and selected 10 machine learning algorithms for AD classifiers according to the suggestion of previous studies (Kringel et al, 2018;Tunvirachaisakul et al, 2018;Xu et al, 2018;Chen et al, 2019;Shigemizu et al, 2019;So et al, 2019;Bi et al, 2020;Yaman et al, 2020). Then we optimized the AD classifiers through parameter adjustment and selected 30% of the data as the test.…”

High‐dimensional integrative copula discriminant analysis for multiomics data

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