Predict the tertiary structure of protein with flexible neural tree

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7390 LNAI, Issue , 2012, Pages 324-331

  Shao, Guangting ^a   Chen, Yuehui ^a  

^a UNIVERSITY OF JINAN   (China)

Author keywords

FNT; PCC; RQA; Tertiary structure of protein; Tree classification model

Indexed keywords

CLASSIFICATION MODELS; FNT; PCC; RQA; TERTIARY STRUCTURE OF PROTEINS;

AMINO ACIDS; BIOINFORMATICS; FEATURE EXTRACTION; FORECASTING; FORESTRY; INTELLIGENT COMPUTING;

PROTEINS;

ALGORITHMS; AMINO ACIDS; ARTIFICIAL INTELLIGENCE; MATHEMATICAL MODELS; NEURAL NETWORKS; PROTEINS;

EID: 84864982274     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31576-3_42     Document Type: Conference Paper

References (17)

1. Levitt, M., Chothia, C.: Structural Patterns in Globular Proteins. Nature 261, 552-558 (1976)
2. Kumar, M., Bhasin, M., Natt, N.K., Raghava, G.P.S.: BhairPred: Prediction of B-hairpins in A PROtein From Multiple Alignment Information using ANN and SVM techniques. Nucleic Acids Res. 33, 154-159 (2005)
3. Metfessel, B.A., Saurugger, P.N., Connelly, D.P., Rich, S.T.: Cross-validation of Protein Structural Class Prediction using Statistical clustering and neural networks. Protein Sci. 1171-1182 (1993)
4. Cai, Y.D., et al.: Prediction of Protein Structural Classes by Support Vector Machines. Computers & Chemistry 26(3), 293-296 (2002)
5. Chen, Y.H., Abraham, A., Yang, J.: Feature Selection and Intrusion Detection Using Hybrid Flexible Neural Tree, pp. 439-444. Springer, Heidelberg (2005)
6. Salustowiczand, R., Schmidhuber, J.: Probabilistic Incremental Program Evolution. Evolutionary Computation 5(2), 123-141 (1997)
7. Wang, Z.X., Yuan, Z.: How good is The Prediction of Protein Structural Class by The Component-coupled Method. Proteins 38, 165-175 (2000)
8. Chen, K., Kurgan, L.A., Ruan, J.: Prediction of Protein Structural Class using Novel Evolutionary Collocation-based Sequence Representation. J. Comput. Chem. 29, 1596-1604 (2008)
9. Chen, C., Chen, L.X., et al.: Predicting protein Structural Class Based on Multi-features Fusion. Journal of Theoretical Biology 253, 388-392 (2008)
10. Yang, J.Y., Deogun, J.: Classifying G Protein-Coupled Receptors with Multiple Physicochemical Properties. Chem-Bio Informatics Journal 8, 49-57 (2008)
11. Giuliani, A., Sirabella, P., Benigni, R., Colosimo, A.: Mapping Protein Sequence Spaces by Recurrence: A Case Study On Chimeric Structures. Protein Eng. 13, 671-678 (2000)
12. Yang, J.Y., Peng, Z.L., et al.: Prediction of Protein Structural Classes by Recurrence Quantification Analysis Based on Chaos Game Representation. J. Theor. Biol. 257(4), 618-626 (2009)
13. Wang, Z.X., Yuan, Z.: How Good Is The Prediction of Protein Structural Class by The Component-coupled Method? Pattern Recogn. 38, 165-175 (2000)
14. Kurgan, L.A., Homaeian, L.: Prediction of Structural Classes for Protein Sequences and Domains-Impact of Prediction Algorithms, Sequence Representation and Homology, and Test Procedures on Accuracy. Pattern Recogn. 39, 2323-2343 (2006)
15. Kedarisetti, K.D., Kurgan, L.A., Dick, S.: Classifier Ensembles for Protein Structural Class Prediction with Varying Homology. Biochem. Biophys. Res. Commun. 348, 981-988 (2006)
16. Zhang, T.L., Ding, Y.S., Chou, K.C.: Prediction Protein Structural Classes with Pseudoamino Acid Composition: Approximate Entropy and Hydrophobicity Pattern. J. Theor. Biol. 250, 186-193 (2008)
17. Chen, K., Kurgan, A., Ruan, J.S.: Prediction of Protein Structural Class using Novel Evolutionary Collocation-Based Sequence Representation. J. Computational Chemistry 29, 1596-1604 (2008)