RESUMO
Entropy-based divergence measures have provided an impelling tool in evaluating sequence complexity, predicting CpG island, and detecting borders between coding and non-coding DNA regions etc. In this paper, two new divergence measures: the alpha-KL divergence and the alpha-Jensen-Shannon divergence were defined and a coarse-graining vector of amino acids- corresponding codons was proposed according to codons GC-content, in order to improve the computational approach to finding borders between coding and non-coding in rice. A comparison of the accuracies gained by different vectors (the Jensen-Shannon divergence, the Jensen-Renyi divergence, the alpha-KL divergence and the alpha-Jensen -Shannon divergence) showed that recognition efficiency based on the new information measures with the vector coarse-graining increase by 4-5 times than that of Bernaola's method in the 'stop codon' of coding regions in rice.
