Skip to main content
SpringerLink
Log in

Moment Vector Encoding of Protein Sequences for Supervised Classification

  • Conference paper
  • First Online:
Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (PACBB 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1005 ))

  • 445 Accesses

Abstract

Automated prediction of biological attributes of protein sequences with machine learning methods depends on a well-suited protein representation. A central challenge is to represent variable-length sequences as fixed-length feature vectors. In this paper we introduce a new approach for representing the protein sequences as a fixed length vector based on statistical moments applied directly to the values of physicochemical properties of amino acids. The results show that this approach of encoding gives higher prediction accuracy on four benchmarks compared to the previous approaches that applied moments of complex descriptors extracted from the physicochemical properties, and even better than the PseAAC encoding method. The best results are achieved by removing highly correlated features with principal component analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Almen, M., Nordström, K., Fredriksson, R., Schioth, H.: Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol. (2009)

    Google Scholar 

  2. Alpaydın, E.: Introduction to Machine Learning. The Adaptive Computation and Machine Learning Series, 2nd edn. Massachusetts Institute of Technology (2010)

    Google Scholar 

  3. Ayyash, M., Tamimi, H., Ashhab, Y.: Developing a powerful in Silico tool for the discovery of novel caspase-3 substrates: a preliminary screening of the human proteome. BMC Bioinf. (2012)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  5. Cangelosi, R., Goriely, A.: Component retention in principal component analysis with application to cDNA microarray data. Biol. Dir. 2(2) (2007)

    Google Scholar 

  6. Chou, C.: Prediction of protein cellular attributes using pseudo-amino-acid composition. In: PROTEINS: Structure, Function, and Genetic, pp. 246–255 (2001)

    Article  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  8. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  9. Georgiev, A.: Interpretable numerical descriptors of amino acid space. J. Comput. Biol. 16(5) (2009)

    Article  Google Scholar 

  10. Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, New York (2002)

    MATH  Google Scholar 

  11. Kumar, M., Gromiha, M.M., Raghava, G.P.S.: Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinf. 8 (2007)

    Article  Google Scholar 

  12. Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X., Chou, K.C.: iDNA-Prot—dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE 9 (2014)

    Article  Google Scholar 

  13. Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure 405(2), 442–451 (1975)

    Article  Google Scholar 

  14. McKee, M., McKee, J.: Biochemistry: The Molecular Basis of Life, 5th edn. Oxford University Press, Oxford (2011)

    MATH  Google Scholar 

  15. Park, K., Gromiha, M., Horton, P., Suwa, M.: Discrimination of outer membrane proteins using support vector machines. Bioinformatics 21, 223–229 (2005)

    Google Scholar 

  16. Qu, K., Han, K., Wu, S., Wang, G., Wei, L.: Identification of DNA-binding proteins using mixed feature representation methods. Molecules 10 (2017)

    Google Scholar 

  17. Rognvaldsson, T., You, L., Garwicz, D.: State of the art prediction of HIV-1 protease cleavage sites. Bioinformatics 31 (2015)

    Article  Google Scholar 

  18. Saidi, R., Maddouri, M., Nguifo, E.: Protein sequences classification by means of feature extraction with substitution matrices. BMC Bioinf. (2010)

    Google Scholar 

  19. Singh, O., Chia-Yu, E.: Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features. BMC Bioinf. 17 (2016)

    Google Scholar 

  20. Sun, D., Xu, C., Zhang, Y.: A novel method of 2D graphical representation for proteins and its application. Commun. Math. Comput. Chem. 75, 431–446 (2016)

    MathSciNet  Google Scholar 

  21. Yau, S.S.T., Yu, C., He, R.: A protein map and its application. DNA Cell Biol. 27 (2008)

    Article  Google Scholar 

  22. Zhou, X., Li, X., Li, M., Lu, X.: Predicting protein functional class with the weighted segmented pseudo-amino acid composition moment vector. Commun. Math. Comput. Chem. 66, 445–462 (2011)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Neural Computation, Ruhr-University Bochum, Bochum, Germany

    Haneen Altartouri & Tobias Glasmachers

Authors
  1. Haneen Altartouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tobias Glasmachers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haneen Altartouri .

Editor information

Editors and Affiliations

  1. Edificio Politécnico, Escuela Superior de Ingeniería Informática,Campus Universitario As Lagoas , Ourense, Spain

    Florentino Fdez-Riverola

  2. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  3. Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Kelantan, Malaysia

    Mohd Saberi Mohamad

  4. Department of Computer Science and Software Engineering Leader, Data Science Research Group, College of Information Technology (CIT) United Arab Emirates University (UAEU), Al Ain, United Arab Emirates

    Nazar Zaki

  5. IBSAL/BISITE Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    José A. Castellanos-Garzón

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Altartouri, H., Glasmachers, T. (2020). Moment Vector Encoding of Protein Sequences for Supervised Classification. In: Fdez-Riverola, F., Rocha, M., Mohamad, M., Zaki, N., Castellanos-Garzón, J. (eds) Practical Applications of Computational Biology and Bioinformatics, 13th International Conference. PACBB 2019. Advances in Intelligent Systems and Computing, vol 1005 . Springer, Cham. https://doi.org/10.1007/978-3-030-23873-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation