Learning Weight Assignment in Distance Function for Biological Sequence Feature Vector by Genetic Algorithm

Transforming sequences into numeric feature vectors is a promising method in bio-sequence similarity search. The process of transformation will lost some information of the origin sequence. In order to improve the accuracy, weight assignment can be used. Based on this notion, we proposed an adaptive weighting distance which is based on feature vector that contains three groups of features: count, RPD, and APD of a DNA sequence. In this paper, weighted LI distance is applied for computing the distance between two feature vectors. We compute the average of entropy of two sequences and then assign the weights to count, RPD and APD according to the weight table which is pointed by the variant of entropy of two sequences. The weight tables are then adjusted by genetic algorithms. Experiment shows that such adaptive weight distance mechanism helps reflect the distance between sequences.