@article{(Open Science Index):https://publications.waset.org/pdf/10000073,
	  title     = {Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (% G) for Gene Silencing},
	  author    = {Reena Murali and  David Peter S.},
	  country	= {},
	  institution	= {},
	  abstract     = {The small interfering RNA (siRNA) alters the
regulatory role of mRNA during gene expression by translational
inhibition. Recent studies show that upregulation of mRNA because
serious diseases like cancer. So designing effective siRNA with good
knockdown effects plays an important role in gene silencing. Various
siRNA design tools had been developed earlier. In this work, we are
trying to analyze the existing good scoring second generation siRNA
predicting tools and to optimize the efficiency of siRNA prediction
by designing a computational model using Artificial Neural Network
and whole stacking energy (%G), which may help in gene silencing
and drug design in cancer therapy. Our model is trained and tested
against a large data set of siRNA sequences. Validation of our results
is done by finding correlation coefficient of experimental versus
observed inhibition efficacy of siRNA. We achieved a correlation
coefficient of 0.727 in our previous computational model and we
could improve the correlation coefficient up to 0.753 when the
threshold of whole tacking energy is greater than or equal to -32.5
kcal/mol.
},
	    journal   = {International Journal of Bioengineering and Life Sciences},
	  volume    = {9},
	  number    = {1},
	  year      = {2015},
	  pages     = {6 - 12},
	  ee        = {https://publications.waset.org/pdf/10000073},
	  url   	= {https://publications.waset.org/vol/97},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 97, 2015},
	}