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Enhanced analytical power of SDS‐PAGE using machine learning algorithms
Author(s) -
Supek Fran,
Peharec Petra,
KrsnikRasol Marijana,
Šmuc Tomislav
Publication year - 2007
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200700555
Subject(s) - computer science , principal component analysis , artificial intelligence , ranking (information retrieval) , pattern recognition (psychology) , noise (video) , filter (signal processing) , visualization , support vector machine , machine learning , feature extraction , feature (linguistics) , image (mathematics) , computer vision , linguistics , philosophy
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.