A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.
Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature se...
| Publicado en: | BioMed Research International pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/9/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148594422&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148594422 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/9/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 148594422 148594422 148594422 10.1155/2021/8890513 148594422 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning. aug: au: Zhou, Juan Hu, Linfeng Jiang, Yu Liu, Liyue affil: School of Software, East China Jiaotong University, Nanchang 330013, China sug: subj: Alzheimer's Disease Familial and Genetic Polymorphism, Single Nucleotide Deep Learning Neural Networks (Computer) Brain Pathology Alzheimer's Disease Diagnosis Human Random Forest Magnetic Resonance Imaging Brain Physiopathology Pearson's Correlation Coefficient Multiple Regression ROC Curve Alzheimer's Disease Etiology ab: Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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