A Localized-Statistic-Based Approach for Biomarker Identification of Omics Data
- 1 Beijing Aerospace Control Center, Beijing, China
- 2 Beijing Aerospace Control Center, Beijing, China
- 3 Beijing Aerospace Control Center, Beijing, China
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.
- D. J. Oliver, B. Nikolau and E. S. Wurtele, “Functional Genomics: High-Throughput mRNA, Protein, and Metabolite Analyses,” Elsevier, 2002, pp. 98-106.
- N. Ishii and M. Tomita, “Multi-Omics Data-Driven Systems Biology of E. coli,” Springer, 2009, p. 41.
- S. Smit, H. C. J. Hoefsloot and A. K. Smilde, “Statistical Data Processing in Clinical Proteomics,” Elsevier, 2008, pp. 77-88.
- H. Shin and M. K. Markey, “A Machine Learning Perspective on the Development of Clinical Decision Support Systems Utilizing Mass Spectra of Blood Samples,” Elsevier, 2006, pp. 227-248.
- I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” MIT Press Cambridge, 2003, pp. 1157-1182.
- E. Marchiori, et al., “Feature Selection for Classification with Proteomic Data of Mixed Quality,” 2005, pp. 1-7.
- H. W. Ressom, et al., “Classification Algorithms for Phenotype Prediction in Genomics and Proteomics,” NIH Public Access, p. 691.
- 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.
- Chen, J. J., et al., “Gene Selection with Multiple Ordering Criteria,” BioMed Central Ltd., 2007, p. 74.
- A. Vlahou, et al., “Development of a Novel Proteomic Approach for the Detection of Transitional Cell Carcinoma of the Bladder in Urine,” ASIP, 2001, pp. 1491-1502.
- 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.
- J. M. Koomen, et al., “Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins,” AACR, 2005, pp. 1110-1118.
- J. M. Koomen, et al., “Diagnostic Protein Discovery Using Proteolytic Peptide Targeting and Identification,” John Wiley & Sons, Ltd., Chichester, 2004.
- 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.
- W. Zhu, et al., “Detection of Cancer-Specific Markers Amid Massive Mass Spectral Data,” National Acad Sciences, 2003, pp. 14666-14671.