Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

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作者： Lei,Du ^[1] ; Heng,Huang ^[2] ; Jingwen,Yan ^[1] ; Sungeun,Kim ^[1] ; Shannon L,Risacher ^[1] ; Mark,Inlow ^[3] ; Jason H,Moore ^[4] ; Andrew J,Saykin ^[1] ; Li,Shen ^[1] ; Alzheimer’s Disease Neuroimaging Initiative

作者单位： Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA. ^[1] Department of Computer Science & Engineering, The University of Texas at Arlington, Arlington, TX, USA. ^[2] Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA and. ^[3] Institute for Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. ^[4]

主题词算法(Algorithms);脑(Brain);人类(Humans)

DOI 10.1093/bioinformatics/btw033

PMID 26801960

发布时间 2020-06-23

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Bioinformatics (Oxford, England)

2016年32卷10期

1544-51页

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Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

Bioinformatics (Oxford, England)

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Bioinformatics (Oxford, England)

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Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

Bioinformatics (Oxford, England)

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Bioinformatics (Oxford, England)

相关期刊

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