Improving Biological Sequence Property Distances by Using a Genetic Algorithm

In this work we present a genetic-algorithm-based approach to optimise weighted distance measurements from compositional and physical-chemical properties of biological sequences that allow a significant reduction of the computational cost associated to the distance evaluation, while maintaining a high accuracy when comparing with traditional methodologies. The strategy has a generic and parametric formulation and exhaustive tests have been performed to shown its adaptability to optimise the weights over different compositions of sequence characteristics. These fast-evaluation distances can be used to deal with large set of sequences as is nowadays imperative, and appear as an important alternative to the traditional and expensive pairwise sequence similarity criterions.