Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification

Mei Ling Hou, Shu Lin Wang, Xue Ling Li, Ying Ke Lei

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.

Original languageEnglish (US)
Article number726413
JournalJournal of Biomedicine and Biotechnology
Volume2010
DOIs
StatePublished - Aug 13 2010

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Health, Toxicology and Mutagenesis

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