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.