Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

BMC Genomics

Matthias Dehmer¹  Rui Gao²  Jialin Zhang³  Yusen Zhang³  Hanxiao Xu³  Wei Chen³  Da Xu³ 

[1] Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus, Steyr, Austria;College of Computer and Control Engineering, Nankai University, 300071, Tianjin, China;Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol, Austria;School of Control Science and Engineering, Shandong University, 250061, Jinan, China;School of Mathematics and Statistics, Shandong University, 264209, Weihai, China;

关键词: Feature selection;    Machine learning;    Classification;    Biomarker;    Therapeutic target;    Clustering;   

DOI  :  10.1186/s12864-020-07038-3

来源: Springer

【 摘 要 】

BackgroundThe small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field.ResultsIn this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy.ConclusionsThe proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

【 授权许可】

CC BY