Title :
Predicting protein structural class with Ensemble of Flexible Neural Tree
Author :
Cai, Nana ; Chen, Yuehui
Author_Institution :
Sch. of Control Sci. & Eng., Univ. of Jinan, Jinan, China
Abstract :
The protein structural class plays an important role in protein science. In this paper, a new method for predicting protein structural class based Flexible Neural Tree Ensemble (FNTE) is introduced. The structure of Flexible Neural Tree (FNT) is developed by Probabilistic Incremental Program Evolution (PIPE) and the parameters are optimized by Particle Swarm Optimization (PSO) algorithm. The one thousand six hundred and seventy three protein sequence (25PDB) is used as the dataset. The experiment data is validated by tenfold cross validation. The experiment result shows our method can improve the predictive accuracy rate.
Keywords :
biology computing; neural nets; particle swarm optimisation; probability; proteins; trees (mathematics); FNTE; PIPE; PSO algorithm; flexible neural tree ensemble; particle swarm optimization; probabilistic incremental program evolution; protein structural class; Amino acids; Artificial neural networks; Biological system modeling; Encoding; Neurons; Prediction algorithms; Proteins; Flexible Neural Tree; Particle Swarm Optimization; Probabilistic Incremental Program Evolution; insert; styling;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583139
