A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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наукова стаття, опублікована у 2017

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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P1476

A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

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P2093

Andrew J. Saykin

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Kefei Liu

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Lei Du

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Lei Guo

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Li Shen

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Xiaohui Yao

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Yuming Huang

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P2860

Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

Limitations of the human reference genome for personalized genomics

Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis

Sparse canonical correlation analysis with application to genomic data integration.

Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort.

The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core.

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

A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.

Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach.

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210-219

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scholarly article

P356

10.1007/978-3-319-67675-3_19

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P407

English

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Shannon L. Risacher

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2017-01-01T00:00:00Z

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P921

big data