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fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

Jiaxing LinAlexander SibleyIvo ShterevAndrew NixonFederico InnocentiCliburn ChanDadong Zhang
Published in: BMC bioinformatics (2019)
fastJT is an open-source R extension package, applying the Jonckheere-Terpstra statistic for robust feature selection for machine learning and association studies. The package implements an efficient algorithm which leverages internal information among the samples to avoid unnecessary computations, and incorporates shared-memory parallel programming to further boost performance on multi-core machines.
Keyphrases
  • machine learning
  • genome wide association
  • artificial intelligence
  • deep learning
  • big data
  • case control
  • working memory
  • health information
  • healthcare
  • social media
  • neural network