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izvor podataka: crosbi

Enhanced analytical power of SDS-PAGE using machine learning algorithms (CROSBI ID 133865)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Supek, Fran ; Peharec, Petra ; Krsnik-Rasol, Marijana ; Šmuc, Tomislav Enhanced analytical power of SDS-PAGE using machine learning algorithms // Proteomics, 8 (2008), 1; 28-31. doi: 10.1002/pmic.200700555

Podaci o odgovornosti

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

engleski

Enhanced analytical power of SDS-PAGE using machine learning algorithms

We aim to demonstrate that a complex plant tissue protein mixture can be reliably 'fingerprinted’ by running conventional one-dimensional 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.

support vector machines ; principal component analysis ; one dimensional gel electrophoresis ; data mining ; differential protein expression

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Podaci o izdanju

8 (1)

2008.

28-31

objavljeno

1615-9853

10.1002/pmic.200700555

Povezanost rada

Biologija, Računarstvo, Biotehnologija

Poveznice
Indeksiranost