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Proteomics. 2008 Jan;8(1):28-31.

Enhanced analytical power of SDS-PAGE using machine learning algorithms.

Author information

1
Laboratory for Information Systems, Division of Electronics, Rudjer Boskovic Institute, Bijenicka cesta 54, Zagreb, Croatia.

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.

PMID:
18046695
DOI:
10.1002/pmic.200700555
[Indexed for MEDLINE]

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