Enhanced analytical power of SDS-PAGE using machine learning algorithms (CROSBI ID 133865)
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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
Povezanost rada
Biologija, Računarstvo, Biotehnologija