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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important “subregion gene pairs”. The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

Details

Title
Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
Author
Li, Jin 1 ; Liu, Wenjie 1 ; Cao, Luolong 1 ; Luo, Haoran 1 ; Xu, Siwen 1 ; Bao, Peihua 1 ; Meng, Xianglian 2 ; Liang, Hong 1 ; Fang, Shiaofen 3 

 College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; [email protected] (J.L.); [email protected] (W.L); [email protected] (L.C.); [email protected] (H.L.); [email protected] (S.X.); [email protected] (P.B.) 
 School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; [email protected] 
 Department of Computer and Information Science, Indiana University-Purdue, University Indianapolis, Indianapolis, IN 46202, USA 
First page
683
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20734425
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2532341220
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.