Predict the tertiary structure of protein with flexible neural tree

Authors:
Guangting Shao;Yuehui Chen
Affiliations:
Computational Intelligence Lab, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China;Computational Intelligence Lab, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 3
Cited 0

Prediction of structural classes for protein sequences and domains-Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy

Pattern Recognition
Probabilistic incremental program evolution

Evolutionary Computation
Feature selection and intrusion detection using hybrid flexible neural tree

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Predicting the tertiary structure of protein from its primary amino acid sequence is a challenging mission for bioinformatics. In this paper we proposes a novel approach of predicting the tertiary structure of protein using the flexible neural tree (FNT) to construct a tree classification model. Two feature extraction methods (the physicochemical composition (PCC)) and the recurrence quantification analysis (RQA)) are employed to extract the features of protein sequence. To value the efficiencies of the proposed method we select two benchmark protein sequence datasets (1189 dataset and 640 dataset), as the test data set. The experimental results show that the proposed method is efficient for the protein structure prediction.