ArticlePloS one2015
Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods.Scientific reports · 2021Article
- Computed tomography radiomic features hold prognostic utility for canine lung tumors: An analytical study.PloS one · 2021Article
- GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies.BioMed research international · 2021Article
- A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.Journal of medical Internet research · 2020Article
- Article
- Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods.Oncology letters · 2019Article
- Identification of subtype-specific prognostic signatures using Cox models with redundant gene elimination.Oncology letters · 2018Article
- Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients.Oncology letters · 2017Article
- Eight potential biomarkers for distinguishing between lung adenocarcinoma and squamous cell carcinoma.Oncotarget · 2017Article
- Identification of prognostic genes and gene sets for early-stage non-small cell lung cancer using bi-level selection methods.Scientific reports · 2017Article
- Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.BioMed research international · 2016Article
- Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography.Journal of computer assisted tomographyArticle
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1 author.
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Abstract
The existence of fundamental differences between lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.
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