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Reproducible analysis of disease space via principal components using the novel R package syndRomics.

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第一作者： Abel,Torres-Espín

第一单位： Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.;Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.;Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States.

作者： Abel,Torres-Espín ^[1] ; Austin,Chou ^[1] ; J Russell,Huie ^[1] ; Nikos,Kyritsis ^[1] ; Pavan S,Upadhyayula ^[2] ; Adam R,Ferguson ^[3]

作者单位： Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.;Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.;Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States. ^[1] School of Medicine, University of California San Diego (UCSD), San Diego, United States. ^[2] Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.;Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.;Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States.;San Francisco VA Health Care System, San Francisco, United States. ^[3]

关键词 R package computational biology disease pattern discovery medicine multivariate analysis none nonlinear PCA principal component analysis pca syndromics systems biology

医学主题词计算生物学(Computational Biology);人类(Humans);主成分分析(Principal Component Analysis);公共卫生(Public Health);软件(Software);综合征(Syndrome)

DOI 10.7554/eLife.61812

PMID 33443012

发布时间 2022-02-01

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