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Title

An ANN-GA model based promoter prediction in Arabidopsis thaliana using tilling microarray data

 

Authors

Hrishikesh Mishra, Nitya Singh, Krishna Misra, Tapobrata Lahiri*

 

Affiliation

Division of Applied Sciences and Indo-Russian Centre for Biotechnology, Indian Institute of Information Technology, Allahabad, India

 

Email

tlahiri@iiita.ac.in; *Corresponding author

 

Article Type

Prediction model

 

Date

Received February 10, 2011; Accepted May 09, 2011; Published June 06, 2011

 

Abstract

Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes.

 

Keywords

core promoter, TATA box, artificial neural network, class membership.

 

Citation

Mishra et al. Bioinformation 6(6): 240-243 (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.