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 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.
Supek
Fran
F
Laboratory for Information Systems, Division of Electronics, Rudjer Boskovic Institute, Bijenicka cesta 54, Zagreb, Croatia.
Peharec
Petra
P
Krsnik-Rasol
Marijana
M
Smuc
Tomislav
T
D000465:Algorithms; D031215:Armoracia; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D004591:Electrophoresis, Polyacrylamide Gel / Q000379:methods*; D010363:Pattern Recognition, Automated; D010449:Peptide Mapping / Q000379:methods; D010940:Plant Proteins / Q000032:analysis; D010941:Plant Tumors; D025341:Principal Component Analysis
D010940:Plant Proteins