Visualization-aided classification ensembles discriminate lung adenocarcinoma and squamous cell carcinoma samples using their gene expression profiles

Ao Zhang, Chi Wang, Shiji Wang, Liang Li, Zhongmin Liu, Suyan Tian

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Introduction: The widespread application of microarray experiments to cancer research is astounding including lung cancer, one of the most common fatal human tumors. Among non-small cell lung carcinoma (NSCLC), there are two major histological types of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC).

Results: In this paper, we proposed to integrate a visualization method called Radial Coordinate Visualization (Radviz) with a suitable classifier, aiming at discriminating two NSCLC subtypes using patients' gene expression profiles. Our analyses on simulated data and a real microarray dataset show that combining with a classification method, Radviz may play a role in selecting relevant features and ameliorating parsimony, while the final model suffers no or least loss of accuracy. Most importantly, a graphic representation is more easily understandable and implementable for a clinician than statistical methods and/or mathematic equations.

Conclusion: To conclude, using the NSCLC microarray data presented here as a benchmark, the comprehensive understanding of the underlying mechanism associated with NSCLC and of the mechanisms with its subtypes and respective stages will become reality in the near future.

Original languageEnglish
Article numbere110052
JournalPLoS ONE
Volume9
Issue number10
DOIs
StatePublished - Oct 15 2014

Bibliographical note

Publisher Copyright:
© 2014 Zhang et al.

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (all)
  • Agricultural and Biological Sciences (all)
  • General

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