Author : P.Deepa 1
Date of Publication :6th March 2018
Abstract: - Gene Ontology (GO) is a structured repository of conception that are associated to one or more gene products through a process referred to as annotation. There are different approaches of analysis to get bio information. One of the analysis is the use of Association Rules (AR) which discovers biologically relevant associations between terms of GO. In existing work we extract weighted Association Rules from Ontology Based annotated datasets by using GO- W AR(Gene Ontology-based Weighted Association Rules).We here adapt the MOAL algorithm to mine cross-ontology association rules, i.e. rules that involve GO terms present in the three sub-ontologies of GO. We are proposing cross ontology to manipulate the Protein values from three sub ontologies for identifying the gene attacked disease. Also our proposed system, focus on intrinsic and extrinsic. Based on cellular component. Molecular function and biological process values intrinsic and extrinsic calculation would be manipulated. In this Paper, We done the Co-Regulatory modules between miRNA (microRNA), TF(Transcription Factor) and gene on functionlevel with multiple genomic data.. We compare the regulations between miRNA-TF interaction, TF-gene interactions and gene-miRNA interaction with the help of integration technique. These interaction could be taken the genetic disease like breast cancer, etc.. Iterative Multiplicative Updating Algorithm is used in our paper to solve the optimization module function for the above interactions. After that interactions, we compare the regulatory modules and protein value for gene and generate Bayesian rose tree for efficiency of our result.
Reference :
-
- 0. Hobert, "Gene regulation by transcription factors and micrornas," Science, vol. 319, no. 5871, pp. 1785-1786, 2008.
- L. He and G. J. Hannon, "Micrornas: small rnas with a big role in gene regulation," Nature Reviews Genetics, vol. 5, no. 7,pp. 522-531,2004.
- J. LU, G. Getz, and E. A. Miska, "Microrna expression profiles classify human cancers," nature, vol. 435, no. 9, pp. 834-838, Jun 2005.
- J. M. Vaquerizas, S. K. Kummerfeld, S. A. Teichmann, and N. M. Luscombe, "A census of human transcription factors: function, expression and evolution," Nature Reviews Genetics, vol. 10, no. 4, pp. 252-263, 2009.
- T. I. Lee, N. J. Rinaldi, F. Robert, D. T. Odom, Z. BarJoseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M. Thomp- son, and I. Simon, "Transcriptional regulatory networks in Saccharomyces cerevisiae," science, vol. 298, no. 5594, pp. 799-804, 2002
- R. Shalgi, D. Lieber, M. Oren, and Y. Pilpel, "Global and local architecture of mammalian micro rna transcription factor regulatory network," PLoS Comput Bioi, vol. 3, no. 7, p. el31, 2007.
- Y. Zhou, J. Ferguson, J. T. Chang, and Y.Kluger,"Interandintra-combinatorial regulation by transcription factors and micrornas," BMC genomics, vol. 8, no. 1, p. 1, 2007.
- C.-Y. Chen, S.-T. Chen, C.-S. Fuh, H.-F. Juan, and H.- C. Huang, "Co-regulation of transcnptwn factors and micrornas in human transcriptional regulatory network," BMC bioinformatics, vol. 12, no. 1, p. 1, 2011.