Detecting and sorting targeting peptides with neural networks and support vector machines.
This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).
School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au
John
J
Hawkins
Mikael
M
Bodén
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D016571:Neural Networks, Computer; D010940:Plant Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D021382:Protein Sorting Signals / Q000235:genetics; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D013347:Subcellular Fractions / Q000378:metabolism
D010940:Plant Proteins; D021382:Protein Sorting Signals; D011506:Proteins