The Universal Similarity Metric, Applied to Contact Maps Comparison in A Two-Dimensional Space

摘要: Comparing protein structures based on their contact maps is an important problem in structural proteomics. Building a system for reconstructing tertiary from one of the motivations devising novel map comparison algorithms. Several methods that address have been designed which are briefly discussed this thesis. However, they suggest scoring schemes do not satisfy two characteristics “metricity” and “universality”. In research we investigate applicability Universal Similarity Metric (USM) to problem. The USM information theoretical measure concept Kolmogorov complexity. ultimate goal use case-based reasoning predict predicted maps. fact will be used such ones sequences noise-free, implies should noise-sensitivity USM. This first attempt study noise-tolerance research, as implementation converted two-dimensional data (contact maps) one-dimensional (strings). results motivated us circumvent dimension reduction our second implement Our i suggested method thesis has advantage obtaining noise tolerant. We assess effectiveness tolerance by testing different against noise-contaminated versions distinguished data-sets.