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NIMAA: an R/CRAN package to accomplish NomInal data Mining AnAlysis

View ORCID ProfileMohieddin Jafari, Cheng Chen, View ORCID ProfileMehdi Mirzaie, View ORCID ProfileJing Tang
doi: https://doi.org/10.1101/2022.01.13.475835
Mohieddin Jafari
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland
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  • For correspondence: mohieddin.jafari@helsinki.fi
Cheng Chen
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland
2Department of Computer Science, University of Helsinki, Helsinki 00560, Finland
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Mehdi Mirzaie
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland
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Jing Tang
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland
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  • For correspondence: mohieddin.jafari@helsinki.fi
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Article Information

doi 
https://doi.org/10.1101/2022.01.13.475835
History 
  • January 18, 2022.

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  • You are currently viewing Version 1 of this article (January 18, 2022 - 10:00).
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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.

Author Information

  1. Mohieddin Jafari1,*,
  2. Cheng Chen1,2,
  3. Mehdi Mirzaie1 and
  4. Jing Tang1,*
  1. 1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland
  2. 2Department of Computer Science, University of Helsinki, Helsinki 00560, Finland
  1. ↵*To whom correspondence should be addressed: mohieddin.jafari{at}helsinki.fi
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Posted January 18, 2022.
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NIMAA: an R/CRAN package to accomplish NomInal data Mining AnAlysis
Mohieddin Jafari, Cheng Chen, Mehdi Mirzaie, Jing Tang
bioRxiv 2022.01.13.475835; doi: https://doi.org/10.1101/2022.01.13.475835
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NIMAA: an R/CRAN package to accomplish NomInal data Mining AnAlysis
Mohieddin Jafari, Cheng Chen, Mehdi Mirzaie, Jing Tang
bioRxiv 2022.01.13.475835; doi: https://doi.org/10.1101/2022.01.13.475835

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