Prediction of Protein Structural Class by Functional Link Artificial Neural Network Using Hybrid Feature Extraction Method

  • Bishnupriya Panda
  • Ambika Prasad Mishra
  • Babita Majhi
  • Minakhi Rout
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


During last few decades’ accurate prediction of protein structural class has been a challenging problem. Efficient and meaningful representation of protein molecule plays a significant role. In this paper Chou’s pseudo amino acid composition along with amphiphillic correlation factor and the spectral characteristics of the protein has been used to represent protein data. Thus a protein sample is represented by a set of discrete components which incorporate both the sequence order and the sequence length effects. On the basis of such a statistical framework a simple functionally linked artificial neural network has been used for structural class prediction.


AAC AmPseAAC DCTAmPseAAC Functional link artificial neural network (FLANN) Protein Domain Structural Class 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chou, K.C.: A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins 21(4), 319–344 (1995)CrossRefGoogle Scholar
  2. 2.
    Du, Q.S., Jiang, Z.Q., He, W.Z., Li, D.P., Chou, K.C.: Amino acid principal component analysis (AAPCA) and its applications in protein structural class prediction. J. Bimol. Struct. Dynam. 23, 635–640 (2006)CrossRefGoogle Scholar
  3. 3.
    Ding, Y.S., Zhang, T.L., Chou, K.C.: Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machines network. Protein Peptide Lett. 14, 811–815 (2007)CrossRefGoogle Scholar
  4. 4.
    Cai, Y.D., Liu, X.J., Xu, X., Zhou, G.P.: Support vector machines for predicting protein structural class. BMC Bioinformatics 2, 3 (2001)CrossRefGoogle Scholar
  5. 5.
    Cai, Y., Zhou, G.: Prediction of protein structural classes by neural network. Biochimie 82(8), 783–785 (2000)CrossRefGoogle Scholar
  6. 6.
    Panda, B., Mishra, A.P., Majhi, B., Rout, M.: Development and performance evaluation of FLANN based model for protein structural class prediction (in press)Google Scholar
  7. 7.
    Sahu, S.S., Panda, G.: A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry 34, 320–327 (2010)CrossRefGoogle Scholar
  8. 8.
    Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications 36, 6800–6808 (2009)CrossRefGoogle Scholar
  9. 9.
    Chou, K.C.: Prediction of protein cellular attributes using pseudo amino acid composition. Proteins 43, 246–255 (2001)CrossRefGoogle Scholar
  10. 10.
    Chou, K.C.: A key driving force in determination of protein structural classes. Biochem. Biophys, Res. Commun. 264, 216–224 (1999)CrossRefGoogle Scholar
  11. 11.
    Zhou, G.P.: An intriguing controversy over protein structural class prediction. J. Protein Chem. 17(8), 729–738 (1998)CrossRefGoogle Scholar
  12. 12.
    Pao, Y.H.: Adaptive pattern recognition & neural networks. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Bishnupriya Panda
  • Ambika Prasad Mishra
  • Babita Majhi
  • Minakhi Rout

There are no affiliations available

Personalised recommendations