title: Improved performance in protein secondary structure prediction by combining multiple predictions.
abstract: In this paper(1) we present a novel framework for protein secondary structure prediction. In this prediction framework, firstly we propose a novel parameterized semi-probability profile, which combines single sequence with evolutionary information effectively. Secondly, different semi-probability profiles are respectively applied as network input to predict protein secondary structure. Then a comparison among these different predictions is discussed in this article. Finally, naïve Bayes approaches are used to combine these predictions in order to obtain a better prediction performance than individual prediction. The experimental results show that our proposed framework can indeed improve the prediction accuracy.
authors.affiliation: Intelligent Computing Lab., Hefei Institute of Intelligent Machines, CAS, P.O. Box 1130, Hefei, Anhui, China. dshuang@iim.ac.cn
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Xin
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019143:Evolution, Molecular; D015233:Models, Statistical; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins