Molecular cancer classification using a meta-sample-based regularized robust coding method

BMC Bioinformatics

Proceedings

Jianwen Fang¹  Liuchao Sun²  Shu-Lin Wang³ 

[1] Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 20850, Rockville, MD, USA;Applied Bioinformatics Laboratory, University of Kansas, 66045, Lawrence, KS, USA;College of Computer Science and Electronics Engineering, Hunan University, 410082, Hunan, China;College of Computer Science and Electronics Engineering, Hunan University, 410082, Hunan, China;Applied Bioinformatics Laboratory, University of Kansas, 66045, Lawrence, KS, USA;

关键词: Singular Value Decomposition;    Feature Extraction Method;    Cancer Dataset;    Cancer Classification;    Generalize Gaussian Distribution;   

DOI  :  10.1186/1471-2105-15-S15-S2

来源: Springer

【 摘 要 】

MotivationPrevious studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data.ResultsIn this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual.ConclusionsExtensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

【 授权许可】

CC BY   
© Wang et al.; licensee BioMed Central Ltd. 2014