Bo Liao , Bo Liao is with the Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China (E-mail: firstname.lastname@example.org).
Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile datasets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.
Bo Liao, Yan Jiang, Wei Liang, Wen Zhu, Lijun Cai, Zhi Cao, "Gene selection using locality sensitive Laplacian score", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2014.2328334