Investigation into the role of sequence-driven-features for prediction of protein structural classes

8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008

Volumn , Issue , 2008, Pages

  Nanuwa, Sundeep Singh ^a   Seker, Huseyin ^a  

^a DE MONTFORT UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ASSESSMENT PROCEDURE; COMPREHENSIVE STUDIES; CROSS-VALIDATION TESTS; DATA SETS; DIPEPTIDE COMPOSITION; FEATURE SETS; HIGH QUALITY; INDEPENDENT SET; K-NEAREST NEIGHBOURS; LEAVE-ONE-OUT; NEIGHBOURHOOD; PREDICTIVE ACCURACY; PREDICTIVE MODELS; PREDICTIVE POWER; PREDICTIVE TECHNIQUES; PROTEIN SEQUENCES; PROTEOMIC STUDIES; RESEARCH RESULTS; STRUCTURAL CLASS;

AMINES; BIOINFORMATICS; HUMAN REHABILITATION ENGINEERING;

SET THEORY;

EID: 67549117626     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2008.4696703     Document Type: Conference Paper

References (26)

1. Gromiha, M.M. and S. Selvaraj, Protein secondary structure prediction in different structural classes. Protein Engineering, 1998.11(4): p. 249-51.
2. Chou, K.C. and C.T. Zhang, Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology, 1995. 30(4): p. 275-349.
3. Bahar, I., et al., Understanding the recognition of protein structural classes by amino acid composition. Proteins: Structure, Function and Genetics, 1997. 29(2): p. 172-185.
4. Levitt, M. and C. Chothia, Structural patterns in globular proteins. Nature, 1976. 261(5561): p. 552-558.
5. Luo, R.Y., Z.P. Feng, and J.K. Liu, Prediction of protein structural class by amino acid and polypeptide composition. European Journal of Biochemistry, 2002. 269(17): p. 4219-4225.
6. Cai, Y.D., et al., Support vector machines for predicting protein structural class. BMC bioinformatics [electronic resource], 2001. 2(1): p. 3.
7. Jin, L., W. Fang, and H. Tang, Prediction of protein structural classes by a new measure of information discrepancy. Computational Biology and Chemistry, 2003. 27(3): p. 373-380.
8. Cai, Y.-D., et al., Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology, 2006. 238(1): p. 172-176.
9. Chen, C, et al., Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Analytical Biochemistry, 2006. 357(1): p. 116-121.
10. Du, Q.S., et al., Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction. Journal of Biomolecular Structure & Dynamics, 2006. 23(6): p. 635-640.
11. Kedarisetti, K.D., L. Kurgan, and S. Dick, Classifier ensembles for protein structural class prediction with varying homology. Biochemical and Biophysical Research Communications, 2006. 348(3): p. 981-988.
12. Xiao, X., et al., Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor. Journal of Computational Chemistry, 2006. 27(4): p. 478-482.
13. Ding, Y.S., T.L. Zhang, and K.C. Chou, Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein and Peptide Letters, 2007. 14(8): p. 811-815.
14. Jahandideh, S., et al., Novel two-stage hybrid neural discriminant model for predicting proteins structural classes. Biophysical Chemistry, 2007.128(1): p. 87-93.
15. Kurgan, L. and K. Chen, Prediction of protein structural class for the twilight zone sequences. Biochemical and Biophysical Research Communications, 2007. 357(2): p. 453-460.
16. Lin, H. and Q.Z. Li, Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components. Journal of Computational Chemistry, 2007. 28(9): p. 1463-1466.
17. [1] Yu, T., et al., Structural class tendency of polypeptide: A new conception in predicting protein structural class. Physica A: Statistical Mechanics and its Applications, 2007. 386(1): p. 581-589.
18. Ke Chen, L.A.K.J.R., Prediction of protein structural class using novel evolutionary collocation-based sequence representation. Journal of Computational Chemistry, 2008. 9999(9999): p. NA.
19. Zhang, T.-L., Y.-S. Ding, and K.-C. Chou, Prediction protein structural classes with pseudo-amino acid composition: Approximate entropy and hydrophobicity pattern. Journal of Theoretical Biology, 2008. 250(1): p. 186-193.
20. Li, Z.R., et al., PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Research, 2006. 34(WEB. SERV. ISS).
21. Kurgan, L. A. and L. Homaeian, Prediction of structural classes for protein sequences and domains-Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy. Pattern Recognition, 2006. 39(12): p. 2323-2343.
22. Wang, Z.X. and Z. Yuan, How good is prediction of protein structural class by the component- coupled method? Proteins: Structure, Function and Genetics, 2000. 38(2): p. 165-175.
23. Chou, K.C. and G.M. Maggiora, Domain structural class prediction. Protein Engineering, 1998.11(7): p. 523-538.
24. Chandonia, J.M., et al., The ASTRAL Compendium in 2004. Nucleic Acids Research, 2004. 32(DATABASE ISS.).
25. Chou, K.C, Progress in protein structural class prediction and its impact to bioinformatics and proteomics. Current Protein and Peptide Science, 2005. 6(5): p. 423-436.
26. Zhang, Z.H., Z.H. Wang, and Y.X. Wang. Nearest neighbor algorithm for prediction of protein domain structural class, in Proceedings - Eighth International Conference on High-Performance Computing in Asia-Pacific Region, HPC Asia 2005. 2005. Beijing.