Enhanced analytical power of SDS-PAGE using machine learning algorithms

被引：20

作者：

Supek, Fran ^{[1
]}

Peharec, Petra ^{[2
]}

Krsnik-Rasol, Marijana ^{[2
]}

Smuc, Tomislav ^{[1
]}

机构：

[1] Rudjer Boskovic Inst, Div Elect, Informat Syst Lab, Zagreb 10000, Croatia

[2] Univ Zagreb, Fac Sci, Div Biol, Dept Mol Biol, Zagreb 41000, Croatia

来源：

PROTEOMICS | 2008年 / 8卷 / 01期

关键词：

1-D gel electrophoresis; data mining; differential protein expression; principal component analysis; support vector machines;

D O I：

10.1002/pmic.200700555

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We aim to demonstrate that a complex plant tissue protein mixture can be reliably "finger-printed' by running conventional 1-D SDS-PAGE in bulk and analyzing gel banding patterns using machine learning methods. An unsupervised approach to filter noise and systemic biases (principal component analysis) was coupled to state-of-the-art supervised methods for classification (support vector machines) and attribute ranking (ReliefF) to improve tissue discrimination, visualization, and recognition of important gel regions.

引用

页码：28 / 31

页数：4

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