A two-stage SVM architecture for predicting the disulfide bonding state of cysteines
Tóm tắt
Cysteines may form covalent bonds, known as disulfide bridges, that have an important role in stabilizing the native conformation of proteins. Several methods have been proposed for predicting the bonding state of cysteines, either using local context or using global protein descriptors. In this paper we introduce an SVM based predictor that operates in two stages. The first stage is a multi-class classifier that operates at the protein level. The second stage is a binary classifier that refines the prediction by exploiting local context enriched with evolutionary information in the form of multiple alignment profiles. The prediction accuracy of the system is 83.6% measured by 5-fold cross validation, on a set of 716 proteins from the September 2001 PDB Select dataset.
Từ khóa
#Support vector machines #Bonding #Amino acids #Proteins #Bridges #Neural networks #Electronic mail #Support vector machine classification #Accuracy #GenomicsTài liệu tham khảo
10.1002/bip.360221211
10.1109/72.788642
leslie, 2002, The spectrum kernel: A string kernel for SVM protein classification, Proc Pacific Symposium on Biocomputing, 564
10.1002/prot.10047
passerini, 2002, From Margins to Probabilities in Multiclass Learning Problems, Proc 15th European Conf on Artificial Intelligence
platt, 2000, Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, Advances in Large Margin Classifiers
10.1093/nar/25.1.226
vapnik, 1998, Statistical Learning Theory
10.1162/089976602753633402
bridle, 1989, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, Neuro-computing Algorithms Architectures and Applications
10.1002/(SICI)1097-0134(19990815)36:3<340::AID-PROT8>3.0.CO;2-D
10.1093/bioinformatics/17.10.957
10.1002/pro.5560030317
10.1093/bioinformatics/16.3.251
boutilier, 1996, Context-specific independence in Bayesian networks, Proc 12th Conf Uncertainty in Artificial Intelligence, 115
bousquet, 2002, Stability and Generalization, Journal of Machine Learning Research, 2
10.1162/neco.1991.3.1.79