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Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases.

Rosana VeronezeSâmia Cruz Tfaile CorbiBárbara Roque da SilvaCristiane S RochaCláudia V Maurer-MorelliSilvana Regina Perez OrricoJoni Augusto CirelliFernando J Von ZubenRaquel Mantuaneli Scarel-Caminaga
Published in: PloS one (2020)
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.
Keyphrases
  • data analysis
  • gene expression
  • end stage renal disease
  • case report
  • chronic kidney disease
  • ejection fraction
  • newly diagnosed
  • dna methylation
  • prognostic factors
  • genome wide
  • oxidative stress
  • transcription factor