Identifying diagnostic biomarkers of breast cancer based on gene expression data and ensemble feature selection

摘要

In recent years, the identification of biomarkers or signatures based on gene expression profiling data has attracted much attention in bioinformatics. The successful discovery of breast cancer (BRCA) biomarkers will be beneficial in reducing the risk of BRCA among patients for early detection. This paper proposes an Ensemble Feature Selection method to screen biomarkers (abbreviated as EFSmarker) for BRCA from publically available gene expression data. Our proposed biomarker discovery strategy not only utilizes the feature contribution but also considers the prediction accuracy simultaneously, which may also serve as a model for identifying unknown biomarkers for other diseases from high-throughput gene expression data. The source code and data are available at https://github.com/zpliulab/EFSmarker.

李苓玉

博士后研究员

研究方向为生物信息学，包括并不限于：空间转录组学分析、稀疏统计学习和生物标志物识别。