ABC Algorithm as Feature Selection for Biomarker Discovery in Mass Spectrometry Analysis

被引：0

作者：

SyarifahAdilah, M. Y. ^{[1
]}

Abdullah, Rosni ^{[2
]}

Venkat, Ibrahim ^{[2
]}

机构：

[1] Univ Teknol MARA Pulau Pinang, Dept Comp Sci & Math, George Town 13500, Malaysia

[2] Univ Sci Malaysia, Sch Comp Sci, George Town 11800, Malaysia

来源：

2012 4TH CONFERENCE ON DATA MINING AND OPTIMIZATION (DMO) | 2012年

关键词：

feature selection; ABC algorithm; biomarker discovery; mass spectrometry; GENETIC ALGORITHM; PROTEOMICS; SPECTRA; SERUM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (ABC) as a new feature selection technique incorporated with SVM classifier. Results achieved96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.

引用

页码：67 / 72

页数：6

共 26 条

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