Minimizing redundancy among genes selected based on the overlapping analysis

Osama Mahmoud, Andrew Harrison, Asma Gul, Zardad Khan, Metodi V. Metodiev, Berthold Lausen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


For many functional genomic experiments, identifying the most characterizing genes is a main challenge. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on a set of discriminative genes. Analyzing overlapping between gene expression of different classes is an effective criterion for identifying relevant genes. However, genes selected according to maximizing a relevance score could have rich redundancy.We propose a scheme for minimizing selection redundancy, in which the Proportional Overlapping Score (POS) technique is extended by using a recursive approach to assign a set of complementary discriminative genes. The proposed scheme exploits the gene masks defined by POS to identify more integrated genes in terms of their classification patterns. The approach is validated by comparing its classification performance with other feature selection methods, Wilcoxon Rank Sum, mRMR, MaskedPainter and POS, for several benchmark gene expression datasets using three different classifiers: Random Forest; k Nearest Neighbour; SupportVector Machine. The experimental results of classification error rates show that our proposal achieves a better performance.

Original languageEnglish
Title of host publicationAnalysis of Large and Complex Data
EditorsAdalbert F.X. Wilhelm, Hans A. Kestler
PublisherKluwer Academic Publishers
Number of pages11
ISBN (Print)9783319252247
Publication statusPublished - 2016
Externally publishedYes
Event2nd European Conference on Data Analysis, ECDA 2014 - Bremen, Germany
Duration: Jul 2 2014Jul 4 2014

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814


Conference2nd European Conference on Data Analysis, ECDA 2014

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Analysis


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