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.
Laboratory for Information Systems, Division of Electronics, Rudjer Boskovic Institute, Bijenicka cesta 54, Zagreb, Croatia.
Fran
F
Supek
Petra
P
Peharec
Marijana
M
Krsnik-Rasol
Tomislav
T
Smuc
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