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

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. T

SUBMITTER: Mishra H 

PROVIDER: S-EPMC3159145 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature