FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics

@article{Gossmann2017FDRCorrectedSC,
  title={FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics},
  author={Alexej Gossmann and Pascal Zille and Vince D. Calhoun and Yu-Ping Wang},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={37},
  pages={1761-1774}
}
  • Alexej Gossmann, Pascal Zille, +1 author Yu-Ping Wang
  • Published in
    IEEE Transactions on Medical…
    2017
  • Computer Science, Mathematics, Biology, Medicine
  • Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimaging and genomics, where data sets are typically high-dimensional, which means that the number of explanatory variables exceeds the sample size. The false discovery rate (FDR) is a criterion that can be employed to address that issue. Thus it has gained great popularity as a tool for testing multiple hypotheses… CONTINUE READING

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