Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results.

Authors

  • Alejandro Lopez-Rincon
    Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, David de Wied building,Universiteitsweg 99, Utrecht, 3584 CG, The Netherlands. alejandro.lopez@iscpif.fr.
  • Marlet Martinez-Archundia
    Laboratorio de Modelado Molecular, Bioinformática y diseño de fármacos. Departamento de Posgrado. Escuela Superior de Medicina del Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
  • Gustavo U Martinez-Ruiz
    Faculty of Medicine, National Autonomous University of Mexico; Federico Gomez Children's Hospital of Mexico, Mexico City, Mexico.
  • Alexander Schoenhuth
    Life Sciences and Health, CWI, Amsterdam, Netherlands.
  • Alberto Tonda
    UMR 782 GMPA, Université Paris-Saclay, INRA, AgroParisTech, Thiverval-Grignon, France.