title: Protein secondary structure prediction based on an improved support vector machines approach.
abstract: The prediction of protein secondary structure is an important step in the prediction of protein tertiary structure. A new protein secondary structure prediction method, SVMpsi, was developed to improve the current level of prediction by incorporating new tertiary classifiers and their jury decision system, and the PSI-BLAST PSSM profiles. Additionally, efficient methods to handle unbalanced data and a new optimization strategy for maximizing the Q(3) measure were developed. The SVMpsi produces the highest published Q(3) and SOV94 scores on both the RS126 and CB513 data sets to date. For a new KP480 set, the prediction accuracy of SVMpsi was Q(3) = 78.5% and SOV94 = 82.8%. Moreover, the blind test results for 136 non-redundant protein sequences which do not contain homologues of training data sets were Q(3) = 77.2% and SOV94 = 81.8%. The SVMpsi results in CASP5 illustrate that it is another competitive method to predict protein secondary structure.
authors.affiliation: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Hyunsoo
authors.initials: H
authors.lastname: Kim
authors.forename: Haesun
authors.initials: H
authors.lastname: Park
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry
chemical_list: D011506:Proteins