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.
References
Haris, A.: The gene ontology (GO) database & informatics resource. Nucleic Acids Res. 32 (2004)
Young, M., Wakefield, M., Smyth, G., Oshlack, A.: Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. (2010)
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)
Albert, R.: Boolean modeling of genetic regulatory networks. Lect. Notes Phys. 650, 459–481 (2004)
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)
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)
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)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference on Research in Computational Linguistics (ROCLING) (1997)
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)
Vengatesan, K., Selvarajan, S., Pragadeeswaran, S.: The performance analysis of microarray data using occurrence clustering. Int. J. Math. Sci. Eng. 3, 69–75 (2014)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
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)