Enhanced analytical power of SDS-PAGE using machine learning algorithms

Fran Supek; Petra Peharec; Marijana Krsnik-Rasol; Tomislav Šmuc

doi:10.1002/pmic.200700555

Enhanced analytical power of SDS-PAGE using machine learning algorithms

Fran Supek
, Petra Peharec
, Marijana Krsnik-Rasol
, Tomislav Šmuc

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

We aim to demonstrate that a complex plant tissue protein mixture can be reliably "fingerprinted" 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.

Original language	English
Pages (from-to)	28-31
Number of pages	4
Journal	Proteomics
Volume	8
Issue number	1
DOIs	https://doi.org/10.1002/pmic.200700555
State	Published - 1 Jan 2008
Externally published	Yes

Keywords

1-D gel electrophoresis
Data mining
Differential protein expression
Principal component analysis
Support vector machines

ASJC Scopus subject areas

Biochemistry
Molecular Biology

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10.1002/pmic.200700555

Cite this

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AU - Peharec, Petra

AU - Krsnik-Rasol, Marijana

AU - Šmuc, Tomislav

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N2 - We aim to demonstrate that a complex plant tissue protein mixture can be reliably "fingerprinted" 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.

AB - We aim to demonstrate that a complex plant tissue protein mixture can be reliably "fingerprinted" 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.

KW - 1-D gel electrophoresis

KW - Data mining

KW - Differential protein expression

KW - Principal component analysis

KW - Support vector machines

UR - https://www.scopus.com/pages/publications/38149108345

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ER -

Enhanced analytical power of SDS-PAGE using machine learning algorithms

Abstract

Keywords

ASJC Scopus subject areas

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