As per insecticide resistance action committee, the insecticide resistance may be defined as heritable change in the sensitivity of a pest population, which is reflected in the repeated failure of a product to achieve the expected level of control when used according to the level recommendation for that pest species. Extensive use of chemical insecticides has been selecting resistant population of insect species to different insecticides, worldwide. Around 590 insect species have been reported to resist different insecticides till the end of 2014.Several studies have indicated that multiple genes are involved in conferring the resistance to many insect. Characterization of these genes is useful to understand the development of resistance and designing new strategies to minimize the development of resistance. Though most of the earlier studies are dealt with the mutational changes associated with the insecticide resistance, development of computational approach for discriminating insecticide resistance proteins from non-resistant proteins will further supplement the earlier efforts in managing the insecticide resistance.

Our Recent publications

Meher PK , Sahu TK , Rao AR(2016), Prediction of donor splice sites using random forest with a new sequence encoding approach, BioData mining, 9:4

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016), Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features, Algorithms for Molecular Biology, 11:16

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016), A computational approach for prediction of donor splice sites with improved accuracy,Journal of Theoretical Biology, 404: 285294

Meher PK , Sahu TK , Rao AR(2016), Performance evaluation of neural network, support vector machine and random forest for prediction of donor splice sites in rice, Indian Journal of Genetics and Plant Breeding, 76(2): 173-180

Meher PK , Sahu TK , Rao AR(2016), Identi?cation of species based on DNA barcode using k-mer feature vector and Random forest classi?er, Gene, http://dx.doi.org/10.1016/j.gene.2016.07.010

Meher PK , Sahu TK , Rao AR, Wahi, SD(2015), Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome, Journal of Plant Biochemistry and Biotechnology, 24(4): 385-392

Meher PK , Sahu TK , Rao AR, Wahi, SD(2014), A statistical approach for 5 splice site prediction using short sequence motifs and without encoding sequence data, BMC Bioinformatics, 15: 362