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Visualization-aided classification ensembles discriminate lung adenocarcinoma and squamous cell carcinoma samples using their gene expression profiles.
- Source :
- PLoS ONE, Vol 9, Iss 10, p e110052 (2014)
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
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.
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8ff85957a9c24a4c871157f0af5751e4
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pone.0110052