Protein subcellular localization prediction based on compartment-specific biological features.
Prediction of subcellular localization of proteins is important for genome annotation, protein function prediction, and drug discovery. We present a prediction method for Gram-negative bacteria that uses ten one-versus-one support vector machine (SVM) classifiers, where compartment-specific biological features are selected as input to each SVM classifier. The final prediction of localization sites is determined by integrating the results from ten binary classifiers using a combination of majority votes and a probabilistic method. The overall accuracy reaches 91.4%, which is 1.6% better than the state-of-the-art system, in a ten-fold cross-validation evaluation on a benchmark data set. We demonstrate that feature selection guided by biological knowledge and insights in one-versus-one SVM classifiers can lead to a significant improvement in the prediction performance. Our model is also used to produce highly accurate prediction of 92.8% overall accuracy for proteins of dual localizations.
Su
Chia-Yu
CY
Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw
Lo
Allan
A
Chiu
Hua-Sheng
HS
Sung
Ting-Yi
TY
Hsu
Wen-Lian
WL
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006090:Gram-Negative Bacteria / Q000378:metabolism; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D011336:Probability; D021382:Protein Sorting Signals; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D012997:Solvents / Q000737:chemistry
D010455:Peptides; D021382:Protein Sorting Signals; D011506:Proteins; D012997:Solvents