Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest With Brain Imaging and Genetic Data

被引:8
作者
Bi, Xia-An [1 ,2 ,3 ]
Xing, Zhaoxu [1 ,2 ,3 ]
Zhou, Wenyan [1 ,2 ,3 ]
Li, Lou [1 ,2 ,3 ]
Xu, Luyun [4 ]
机构
[1] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language, Changsha 410081, Peoples R China
[2] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China
[3] Hunan Xiangjiang Artificial Intelligence Acad, Changsha 410081, Peoples R China
[4] Hunan Normal Univ, Coll Business, Changsha 410081, Peoples R China
基金
中国国家自然科学基金;
关键词
Correlation; Random forests; Bioinformatics; Functional magnetic resonance imaging; Feature extraction; Imaging; Radio frequency; Imaging genetics; mild cognitive impair- ment; weighted evolutionary random forest; DIMENSIONALITY REDUCTION; CLASSIFICATION; CONNECTIVITY; SELECTION; PROGRESS; FMRI;
D O I
10.1109/JBHI.2022.3151084
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.
引用
收藏
页码:3068 / 3079
页数:12
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