Selection of relevant genes in cancer diagnosis based on their prediction accuracy

Rosalia Maglietta, Annarita D'Addabbo, Ada Piepoli, Francesco Perri, Sabino Liuni, Graziano Pesole, Nicola Ancona

Research output: Contribution to journalArticlepeer-review


Motivations: One of the main problems in cancer diagnosis by using DNA microarray data is selecting genes relevant for the pathology by analyzing their expression profiles in tissues in two different phenotypical conditions. The question we pose is the following: how do we measure the relevance of a single gene in a given pathology? Methods: A gene is relevant for a particular disease if we are able to correctly predict the occurrence of the pathology in new patients on the basis of its expression level only. In other words, a gene is informative for the disease if its expression levels are useful for training a classifier able to generalize, that is, able to correctly predict the status of new patients. In this paper we present a selection bias free, statistically well founded method for finding relevant genes on the basis of their classification ability. Results: We applied the method on a colon cancer data set and produced a list of relevant genes, ranked on the basis of their prediction accuracy. We found, out of more than 6500 available genes, 54 overexpressed in normal tissues and 77 overexpressed in tumor tissues having prediction accuracy greater than 70 % with p-value ≤ 0.05. Conclusions: The relevance of the selected genes was assessed (a) statistically, evaluating the p-value of the estimate prediction accuracy of each gene; (b) biologically, confirming the involvement of many genes in generic carcinogenic processes and in particular for the colon; (c) comparatively, verifying the presence of these genes in other studies on the same data-set.

Original languageEnglish
Pages (from-to)29-44
Number of pages16
JournalArtificial Intelligence in Medicine
Issue number1
Publication statusPublished - May 2007


  • Cancer diagnosis
  • Classification
  • DNA microarray
  • Gene selection
  • Supervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine(all)


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