title: Prediction of protein secondary structure using improved two-level neural network architecture.
abstract: In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.
authors.affiliation: Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Huang
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Guang-Zheng
authors.initials: GZ
authors.lastname: Zhang
authors.forename: Yun-Ping
authors.initials: YP
authors.lastname: Zhu
authors.forename: Yi-Xue
authors.initials: YX
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary