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.
 M. Dakna, et al., “Technical, Bioinformatical and Statistical Aspects of Liquid Chromatography-Mass Spectrometry (LC-MS) and Capillary Electrophoresis-Mass Spectrometry (CE-MS) Based Clinical Proteomics: A Critical Assessment,” Elsevier, 2009, pp. 1250-1258.
 M. J. Campa, et al., “Protein Expression Profiling Identifies Macrophage Migration Inhibitory Factor and Cyclophilin A as Potential Molecular Targets in Non-Small Cell Lung Cancer 1,” AACR, 2003, pp. 1652-1656.
 K. R. Kozak, et al., “Identification of Biomarkers for Ovarian Cancer Using Strong Anion-Exchange ProteinChips: Potential Use in Diagnosis and Prognosis,” National Acad Sciences, 2003, pp. 12343-12348.
 T. C. W. Poon, et al., “Comprehensive Proteomic Profiling Identifies Serum Proteomic Signatures for Detection of Hepatocellular Carcinoma and Its Subtypes,” American Association of Clinical Chemistry, 2003, p. 752-760.
 L. H. Cazares, et al., “Normal, Benign, Preneoplastic, and Malignant Prostate Cells Have Distinct Protein Expression Profiles Resolved by Surface Enhanced Laser Desorption/Ionization Mass Spectrometry 1,” AACR, 2002, pp. 2541-2552.
 J. K. Eng, A. L. McCormack and J. R. Yates Iii, “An Approach to Correlate Tandem Mass Spectra Data of Peptides with Amino Acid Sequences in a Protein Database,” Elsevier Science Pub. Co., New York, 1994, pp. 976-989.