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has been cited by the following article:

• TITLE: A Localized-Statistic-Based Approach for Biomarker Identification of Omics Data

  AUTHORS: Kuan Zhang, He Chen, Yongtao Li

  KEYWORDS: Protein-Omics Data; Biomarker Selection; Localized Statistic; Gaussian Window

  JOURNAL NAME: Engineering, Vol.5 No.10B, December 17, 2013

  ABSTRACT: Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.