Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Zhixi Hu, Congye Tang, Yingxia Liang, Senhao Chang, Xinyue Ni, Shasha Xiao, Xianglian Meng, Bing He () and Wenjie Liu ()
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Zhixi Hu: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Congye Tang: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Yingxia Liang: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Senhao Chang: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Xinyue Ni: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Shasha Xiao: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Xianglian Meng: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China
Bing He: Luddy School of Informatics, Computing and Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Wenjie Liu: School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou 213002, China

Mathematics, 2024, vol. 12, issue 5, 1-20

Abstract: Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers that contribute significantly to the classification of AD is an effective means to accomplish this essential task. However, most machine learning methods used to detect AD biomarkers require lengthy training and are unable to rapidly and effectively detect AD biomarkers. To detect biomarkers for AD accurately and efficiently, we proposed a novel approach using the Multi-Kernel Support Vector Machine (SVM) with Apriori algorithm to mine strongly associated feature sets from functional magnetic resonance imaging (fMRI) and gene expression profiles. Firstly, we downloaded the imaging data and genetic data of 121 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and transformed gene sequences into labeled sequences by encoding the four types of bases (A, T, C, and G) into distinct labels. Subsequently, we extracted the first 130 temporal sequences of brain regions and employed Pearson correlation analysis to construct “brain region gene pairs”. The integration of these data allowed us to explore the correlations between genes and brain regions. To improve classification accuracy and feature selection, we applied the Apriori algorithm to the multi-kernel SVM, dynamically building feature combinations and continuously validating classification results. By iteratively generating frequent itemsets, we obtained important brain region gene pairs. Experimental results show the effectiveness of our proposed approach. The Multi-Kernel SVM with Apriori model achieves an accuracy of 92.9%, precision of 95%, and an F1 score of 95% in classifying brain region-gene pairs within the AD–Late mild cognitive impairment (AD-LMCI) group. The amygdala, BIN1 , RPN2 , and IL15 associated with AD have been identified and demonstrate potential in identifying potential pathogenic factors of AD. The selected brain regions and associated genes may serve as valuable biomarkers for early AD diagnosis and better understanding of the disease’s molecular mechanisms. The integration of fMRI and gene data using the Multi-Kernel SVM–Apriori model holds great potential for advancing our knowledge of brain function and the genetic basis of neurological disorders. This approach provides a valuable tool for neuroscientists and researchers in the field of genomics and brain imaging studies.

Keywords: Alzheimer’s disease; SVM; Apriori algorithm; brain region-gene pairs; frequent itemset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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