Feature Extraction and Classification of Proteomics Data Using Stationary Wavelet Transform and Naive Bayes Classifier

Author

Liu Dan ; Huang Yuan-yuan ; Ma Chen-xiang

Author_Institution

Sch. of Life Sci. & Technol., Xi´an Jiaotong Univ., Xi´an, China

fYear

2010

fDate

18-20 June 2010

Firstpage

1

Lastpage

4

Abstract

The purpose of the current study was to investigate the changes of serum proteome and to discover potential biomarkers from a publicly available proteomic ovarian dataset. A workflow that combines stationary wavelet transform with naive Bayes classifier was presented to select candidate biomarkers form 253 proteomic serum profiles of cancer and control. The method identified correlative mass points and obtained a discriminative pattern with 96.7% sensitivity and 92.7% specificity.

Keywords

Bayes methods; cancer; data analysis; feature extraction; proteomics; wavelet transforms; biomarkers; cancer; feature extraction; naive Bayes classifier; proteomic ovarian dataset; proteomic serum profiles; proteomics data classification; serum proteome changes; stationary wavelet transform; Biomarkers; Cancer; Digital signal processing; Diseases; Electronic mail; Feature extraction; Mass spectroscopy; Noise reduction; Proteomics; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on

Conference_Location

Chengdu

ISSN

2151-7614

Print_ISBN

978-1-4244-4712-1

Electronic_ISBN

2151-7614

Type

conf

DOI

10.1109/ICBBE.2010.5516610

Filename

5516610