Evaluation of descriptor algorithms of biological sequences and distance measures for the intelligent cluster index (ICIx)

Abstract

In hindsight of the previous decades, a rapid growth of data in all fields of life sciences is perceptible. Most notably is the general tendency of retaining well established techniques regarding specific biological requirements and common taxonomies for data classification. Therefore a change in perspective towards advanced technological concepts for persisting, organizing and analyzing these huge amounts of data is essential. The Intelligent Cluster Index (ICIx) is a modern technology capable of indexing multidimensional data through semantic criteria, qualified for this challenge. In this paper methodical approaches for indexing biological sequences with the ICIx are discussed and evaluated. This includes the examination of established methods concentrating on vector transformation as well as outlining the efficiency of different distance measures applied to these vectors. Based on our results, it becomes apparent that position conserving methods are superior to other approaches and that the applied distance measures heavily influence performance and quality.