Protein fold recognition using segmentation-based feature extraction model

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

Volumn 7802 LNAI, Issue PART 1, 2013, Pages 345-354

  Dehzangi, Abdollah ^a,b   Sattar, Abdul ^a,b  

^a GRIFFITH UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

AdaBoost.M1; Ensemble of different Classifiers; Physicochemical based features; Segmented Density; Segmented distribution; SVM

Indexed keywords

ADABOOST.M1; FEATURE EXTRACTION METHODS; PHYSICOCHEMICAL-BASED FEATURES; PROTEIN FOLD RECOGNITION; PROTEIN STRUCTURE PREDICTION; SEGMENTED DISTRIBUTION; SVM;

AMINO ACIDS; DATABASE SYSTEMS; FEATURE EXTRACTION; PROTEIN FOLDING;

SUPPORT VECTOR MACHINES;

EID: 84874632395     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-36546-1_36     Document Type: Conference Paper

References (17)

1. Kavousi, K., Moshiri, B., Sadeghi, M., Araabi, B.N., Moosavi-Movahedi, A.A.: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via pssm. Computational Biology and Chemistry 35(1), 1-9 (2011)
2. Dehzangi, A., Phon Amnuaisuk, S., Ng, K.H., Mohandesi, E.: Protein Fold Prediction Problem Using Ensemble of Classifiers. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 503-511. Springer, Heidelberg (2009)
3. Krishnaraj, Y., Reddy, C.K.: Boosting methods for protein fold recognition: An empirical comparison. In: Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine, pp. 393-396 (2008)
4. Dehzangi, A., Phon-Amnuaisuk, S., Dehzangi, O.: Using random forest for protein fold prediction problem: An empirical study. Journal of Information Science and Engineering 26(6), 1941-1956 (2010)
5. Ding, C., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349-358 (2001)
6. Kurgan, L.A., Zhang, T., Zhang, H., Shen, S., Ruan, J.: Secondary structure-based assignment of the protein structural classes. Amino Acids 35, 551-564 (2008)
7. Gromiha, M.M., Oobatake, M., Sarai, A.: Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins. Biophysical Chemistry 82, 51-67 (1999)
8. Dehzangi, A., Phon-Amnuaisuk, S.: Fold prediction problem: The application of new physical and physicochemical- based features. Protein and Peptide Letters 18(2), 174-185 (2011)
9. Chen, K., Kurgan, L.A.: Pfres: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21), 2843-2850 (2007)
10. Shen, H.B., Chou, K.C.: Predicting protein fold pattern with functional domain and sequential evolution information. Journal of Theoretical Biology 256(3), 441-446 (2009)
11. Yang, J.Y., Chen, X.: Improving taxonomy-based protein fold recognition by using global and local features. Protein 79(7), 2053-2064 (2011)
12. Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717-1722 (2006)
13. Taguchi, Y.H., Gromiha, M.M.: Application of amino acid occurrence for discriminating different folding types of globular proteins. BMC Bioinformatics 8(1), 404 (2007)
14. Mathura, V.S., Kolippakkam, D.: Apdbase: Amino acid physico-chemical properties database. Bioinformation 12(1), 2-4 (2005)
15. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148-156 (1996)
16. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
17. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001)