A Genotype-Based Ensemble Classifier System for Non-Small-Cell Lung Cancer

The heterogeneity of cancer reflects the complexity of genetic mutations. Dissecting the heterogeneity plays an important role in the field of biomarker discovery, targeted therapy and drug designing. As it is time-consuming to identify new biomarkers in biological experiments, various machine learning methods have been developed. However, the current methods are limited because they ignore that patients may correspond to different disease-causing genotypes. In this article, a genotype-based ensemble classifier system (GECS) is proposed which aims to explain pathologies of NSCLC from the view of genotypes and identify the genetic subtypes of tumors. The core strategy of GECS is to construct multiple independent classifiers following the principle that one classifier is constructed based on one genotype of NSCLC. The analysis of synthetic data and three microarray datasets indicated that the proposed method outperforms existing approaches in the identification of genetic subtypes of tumors. The GECS method provides a useful tool for molecular pathology researches for dissecting the heterogeneity of cancer.