Skip to main content
SpringerLink
Log in
Book cover

Frontier and Innovation in Future Computing and Communications pp 785–797Cite as

Functional Annotation of Proteins by a Novel Method Using Weight and Feature Selection

  • Conference paper
  • First Online:

  • 2111 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 301))

Abstract

The definition of the automatic protein function means designating the function with the automation by utilizing the data that already revealed unknown protein function. The demand for analysis on the sequencing technology such as the next generation genome analysis (NGS) and the subsequent genome are on the rise; thus, the need for the method of predicting the protein function automatically has been more and more highlighted. As for the existing methods, the studies on the definition of function between the similar species based on the similarities of sequence have been primarily conducted. However, this paper aims to designate by automatically predicting the function of genome by utilizing InterPro (IPR) that can represent the properties of the protein family, which similarly groups the protein function. Moreover, the gene ontology (GO), which is the controlled vocabulary to describe the protein function comprehensively, is to be used. As for the data used in the experiment, the analysis on properties was conducted in the sparse state that is deflected to one side. Thus, this paper aims to analyze the prediction method for protein function automatically through selecting the features, assigning the data processing and weights and applying a variety of classification methods to overcome that property.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  Google Scholar 

  2. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29

    Article  Google Scholar 

  3. Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R (2004) The gene ontology annotation (GOA) database: sharing knowledge in Uniprot with gene ontology. Nucleic Acids Res 32: D262–D266

    Google Scholar 

  4. Chang CC, Lin CJ (2011). LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27

    Google Scholar 

  5. Chawla N, Bowyer K, Hall L, Kegelmeyer P (2002) SMOTE: synthetic minority over-sampling technique. JAIR 16:321–357

    Google Scholar 

  6. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18):3674–3676 Epub, Aug 4 2005

    Google Scholar 

  7. Freund Y, Schapire R (1996) A short introduction to boosting. J Japan Soc Artif Intell 14(5):771–780

    Google Scholar 

  8. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  9. Hunter S, Jones P, Mitchell A et al (2011) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res 40:D306–D312

    Article  Google Scholar 

  10. John CP Sequential minimal optimization: a fast algorithm for training support vector machines

    Google Scholar 

  11. Koski LB, Gray MW, Lang BF, Burger G (2005) AutoFACT: an automatic functional annotation and classification tool. BMC Bioinf 6:151

    Google Scholar 

  12. Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the fourteenth international conference on machine learning, pp 179–186

    Google Scholar 

  13. Martin DM, Berriman M, Barton GJ (2004) GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinf 5:178

    Google Scholar 

  14. Quevillon E, Silventoinen V, Pillai S et al (2005) InterProScan: protein domains identifier. Nucleic Acids Res 33:W116–W120

    Article  Google Scholar 

  15. Shahib A Al, Breitling R, Gilbert D (2005) Feature selection and the class imbalance problem in predicting protein function from sequence. Appl Bioinf 4(3):195–203

    Google Scholar 

  16. Zehetner G (2003) OntoBlast function: from sequence similarities directly to potential functional annotations by ontology terms. Nucleic Acids Res 31(13):803–3799

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2063006).

Author information

Authors and Affiliations

  1. Samsung Electronics, Suwon, South Korea

    Jaehee Jung & Heung Ki Lee

  2. Gangneung-Wonju National University, Gangwon, South Korea

    Gangman Yi

Authors
  1. Jaehee Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heung Ki Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gangman Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gangman Yi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

  4. Computer Science & Software Engineering, Monmouth University, W. Long Branch, New Jersey, USA

    Mohammad Obaidat

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this paper

Cite this paper

Jung, J., Lee, H.K., Yi, G. (2014). Functional Annotation of Proteins by a Novel Method Using Weight and Feature Selection. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_88

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-8798-7_88

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8797-0

  • Online ISBN: 978-94-017-8798-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation