ArticlePloS one2014
Visualization-aided classification ensembles discriminate lung adenocarcinoma and squamous cell carcinoma samples using their gene expression profiles.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer.Scientific reports · 2019Trial
- Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma.The Journal of international medical research · 2020Article
- A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas.BMC genomics · 2019Article
- Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.BMC genomics · 2018Article
- Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients.Oncology letters · 2017Article
- Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes.Biology direct · 2016Article
- Measurement of quality of life in second-line patients with advanced NSCLC without targetable mutations: a review.Lung cancer management · 2016Review
- On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.BMC cancer · 2016Article
- Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.BioMed research international · 2016Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionThe 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).
resultsIn 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.
conclusionTo 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.
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