Sparse Bayesian variable selection for classifying high-dimensional data

Yang, Aijun1,2; Lian, Heng3; Jiang, Xuejun4; Liu, Pengfei5

2018

Statistics and Its Interface   影响因子和分区

1938-7989

1938-7997

Methods for selecting important genes are of much significance in accurate classification. Owing to the large number of genes and many of them are irrelevant, insignificant or redundant, standard statistical methods do not work well. The modification of existing methods is needed to achieve better analysis of microarray data. We present a stochastic variable selection approach for gene selection with different two level hierarchical prior distributions for regression coefficients. These priors can be used as a sparsity-enforcing mechanism to perform gene selection for classification. Using simulation-based MCMC methods for simulating parameters from the posterior distribution, an efficient algorithm is developed and implemented. This algorithm is robust to the choices of initial values, and produces posterior probabilities of related genes for biological interpretation. To highlight the potential applications of the proposed approach, we provide examples of the well-known colon cancer data and leukemia data in microarray literature.

Sparse priors Stochastic variable selection Classification High-dimensional data

SCI

英语

通讯

Natural Science Research in Jiangsu Province[15KJB110007]

Mathematical & Computational Biology ; Mathematics

Mathematical & Computational Biology ; Mathematics, Interdisciplinary Applications

WOS:000426874600015

INT PRESS BOSTON, INC

Web of Science

1.Nanjing Forestry Univ, Coll Econ & Management, Nanjing, Jiangsu, Peoples R China
2.State Stat Bur, Key Lab Stat Informat Technol & Data Min, Chengdu, Sichuan, Peoples R China
3.City Univ Hong Kong, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
4.South Univ Sci & Technol China, Dept Math, Shenzhen, Peoples R China
5.Jiangsu Normal Univ, Sch Math & Stat, Xuzhou, Peoples R China

Yang, Aijun,Lian, Heng,Jiang, Xuejun,et al. Sparse Bayesian variable selection for classifying high-dimensional data[J]. Statistics and Its Interface,2018,11(2):385-395.

Yang, Aijun,Lian, Heng,Jiang, Xuejun,&Liu, Pengfei.(2018).Sparse Bayesian variable selection for classifying high-dimensional data.Statistics and Its Interface,11(2),385-395.

Yang, Aijun,et al."Sparse Bayesian variable selection for classifying high-dimensional data".Statistics and Its Interface 11.2(2018):385-395.