Adaptive Weighting Distance for Feature Vectors of Biological Sequences

Similarity search in biology sequences has received substantial attention in the past decade. Sequence alignment is the essential task for similar sequence search in bioinformatics. The biological sequence databases have getting larger in past decade, finding sequences similar to the query sequence is a time consuming task. By transforming sequences into numeric feature vectors, we can quickly filter out sequences whose feature vectors are distant to the feature vector of the query sequence. We proposed an adaptive weighting distance which is based on feature vector that contains three groups of features: count, extended relative position dispersion (XRPD), and extended absolute position dispersion (XAPD) of a DNA sequence. Each group has four dimensions for A, C, T, and G. When computing distance between two feature vectors, Euclidean distance and L1 distance are commonly used. In this paper, we use weighted L1 distance for computing the distance between two feature vectors. We derive weights for the four nucleotides from the count group, and apply the weights to both XRPD and XAPD. In other words, if a certain kind of nucleotide appears much frequent than the other kinds of nucleotides, the weight for the kind of nucleotide should also be large in XRPD and XAPD groups. Experiments show that such distance of feature vectors helps reflect the distance between sequences.