Skip to main content

Similarity Measurement of Gene Using Arc Tan Function in Gene Ontology

  • Conference paper
  • First Online:
Advances in Electronics, Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

  • 1875 Accesses

Abstract

The Gene Ontology (GO) is a technique that measures second-hand the semantic relationship of genes using an exponential and arc tan-based technique. The proposed work develops an enhanced shortest path-based measure using GO semantic relationship. The existing work presented an undeviating corridor based on an amalgam appraise of ontological match. Using GO graph and in sequence pleased are factors that improve the effectiveness of transfer functions of semantic connection of gene ontology. The semantic technique evaluates weighted paths for GO similarity measure. Other similarity measures use the shortest path in their calculation but require the specificity of a concept in hybrid measure performance of correlated genes.

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

Access this chapter

Institutional subscriptions

References

  1. Haris, A.: The gene ontology (GO) database & informatics resource. Nucleic Acids Res. 32 (2004)

    Google Scholar 

  2. Young, M., Wakefield, M., Smyth, G., Oshlack, A.: Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. (2010)

    Google Scholar 

  3. Majewski, J., Zawadzki, P., Pickerill, P., Cohan, F., Dowson, C.: Barriers to genetic exchange between bacterial species: streptococcus pneumonia transformation. J. Bacteriol. 1016–1023 (2000)

    Google Scholar 

  4. Albert, R.: Boolean modeling of genetic regulatory networks. Lect. Notes Phys. 650, 459–481 (2004)

    Article  MathSciNet  Google Scholar 

  5. Paul, S., Baxter, D., Byrne, J.: Modeling transcriptional control in gene networks-methods, recent results, and future directions. Bull. Math. Biol. 62(2), 247–292 (2000)

    Article  Google Scholar 

  6. Xu, T., Du, L. Zhou, Y.: Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data. BMC Bioinf. 9 (2008)

    Google Scholar 

  7. Barrett, T., Troup, D., Wilhite, S., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I.F., Soboleva, A., Tom, M., Edgar, R.: NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res. 35 (2006)

    Google Scholar 

  8. Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference on Research in Computational Linguistics (ROCLING) (1997)

    Google Scholar 

  9. Schlicker, A., Domingues, F., Rahnenfuhrer, J., Lengauer, T.: A new measure for functional similarity of gene products based on gene ontology. BMC Bioinf. 7 (2006)

    Google Scholar 

  10. Vengatesan, K., Selvarajan, S., Pragadeeswaran, S.: The performance analysis of microarray data using occurrence clustering. Int. J. Math. Sci. Eng. 3, 69–75 (2014)

    Google Scholar 

  11. Yu, G., Li, F., Qin, Y., Xiaochen, B., Wu, S.W.: GO SemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinf. Appl. Note 26, 976–978 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Vengatesan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vengatesan, K., Mahajan, S.B., Sanjeevikumar, P., Kulkarni, R.M., Moin, S. (2018). Similarity Measurement of Gene Using Arc Tan Function in Gene Ontology. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_83

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4765-7_83

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics