An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer's Disease.
Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation anal...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 13; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
1/6/2022
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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=154540701&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154540701 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 1/6/2022 vid: 13 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 154540701 154540701 154540701 10.3389/fnagi.2021.817520 154540701 ppf: 1 ppct: 13 formats: tig: atl: An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer's Disease. aug: au: Wang, Shuaiqun Wu, Xinqi Wei, Kai Kong, Wei affil: College of Information Engineering, Shanghai Maritime University, Shanghai, China sug: subj: Alzheimer's Disease Diagnosis Biological Markers Neuroradiography Genetics Models, Statistical Human Canonical Correlation Analysis Algorithms Magnetic Resonance Imaging Male Female Aged Aged, 80 and Over Aged: 65+ years Aged, 80 & over Male Female ab: Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer's disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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