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Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest

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, b...

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Autores principales: Li, Jin, Liu, Wenjie, Cao, Luolong, Luo, Haoran, Xu, Siwen, Bao, Peihua, Meng, Xianglian, Liang, Hong, Fang, Shiaofen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147351/
https://www.ncbi.nlm.nih.gov/pubmed/34062866
http://dx.doi.org/10.3390/genes12050683
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author Li, Jin
Liu, Wenjie
Cao, Luolong
Luo, Haoran
Xu, Siwen
Bao, Peihua
Meng, Xianglian
Liang, Hong
Fang, Shiaofen
author_facet Li, Jin
Liu, Wenjie
Cao, Luolong
Luo, Haoran
Xu, Siwen
Bao, Peihua
Meng, Xianglian
Liang, Hong
Fang, Shiaofen
author_sort Li, Jin
collection PubMed
description 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.
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spelling pubmed-81473512021-05-26 Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest Li, Jin Liu, Wenjie Cao, Luolong Luo, Haoran Xu, Siwen Bao, Peihua Meng, Xianglian Liang, Hong Fang, Shiaofen Genes (Basel) Article 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. MDPI 2021-05-01 /pmc/articles/PMC8147351/ /pubmed/34062866 http://dx.doi.org/10.3390/genes12050683 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Li, Jin
Liu, Wenjie
Cao, Luolong
Luo, Haoran
Xu, Siwen
Bao, Peihua
Meng, Xianglian
Liang, Hong
Fang, Shiaofen
Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title_full Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title_fullStr Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title_full_unstemmed Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title_short Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
title_sort hippocampal subregion and gene detection in alzheimer’s disease based on genetic clustering random forest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147351/
https://www.ncbi.nlm.nih.gov/pubmed/34062866
http://dx.doi.org/10.3390/genes12050683
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