A robust and accurate method for feature selection and prioritization from multi-class OMICs data

Vittorio Fortino, Pia Kinaret, Nanna Fyhrquist, Harri Alenius, Dario Greco

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)


Selecting relevant features is a common task in most OMICs data analysis, where the aim is to identify a small set of key features to be used as biomarkers. To this end, two alternative but equally valid methods are mainly available, namely the univariate (filter) or the multivariate (wrapper) approach. The stability of the selected lists of features is an often neglected but very important requirement. If the same features are selected in multiple independent iterations, they more likely are reliable biomarkers. In this study, we developed and evaluated the performance of a novel method for feature selection and prioritization, aiming at generating robust and stable sets of features with high predictive power. The proposed method uses the fuzzy logic for a first unbiased feature selection and a Random Forest built from conditional inference trees to prioritize the candidate discriminant features. Analyzing several multi-class gene expression microarray data sets, we demonstrate that our technique provides equal or better classification performance and a greater stability as compared to other Random Forest-based feature selection methods.

Original languageEnglish
Pages (from-to)e107801
JournalPLoS ONE
Issue number9
Publication statusPublished - 2014
Externally publishedYes
Publication typeA1 Journal article-refereed


  • Algorithms
  • Computational Biology/methods
  • Databases, Genetic
  • Fuzzy Logic
  • Gene Expression
  • Humans


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