Title

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes 

Authors

Vinod Chandra¹*, Rejimoan Ramakrishnan², Shalini Ramanathan² 

Affiliation

¹Department of Computer Applications, College of Engineering Trivandrum, Kerala, India; ²Department of Computer Science, P.S.G. College of Technology, Coimbatore, Tamil Nadu, India 

Email

vinodchandrass@gmail.com; *Corresponding author

Phone

91 471 2515531

Fax

91 471 2598370

Article Type

Prediction model

Date

Received February 07, 2011; Accepted February 17, 2011; Published March 02, 2011

Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An attempt was made to develop a new approach to predict such nsSNPs. This would enhance our understanding of genetic diseases and helps to predict the disease. We detect nsSNPs and all possible and reliable alleles by ANN, a soft computing model using potential SNP information. Reliable nsSNPs are identified, based on the reconstructed alleles and on sequence redundancy. The model gives good results with mean specificity (95.85%), sensitivity (97.40%) and accuracy (96.25%). Our results indicate that ANNs can serve as a useful method to analyze quantitative effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.  

Keywords

SNP, nsSNP, ANN, Tumor suppressor genes

Availability

http://www.snp.mirworks.in

Citation

Chandra et al. Bioinformation 6(1): 41-44 (2011)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.