Master's Thesis from the year 2006 in the subject Computer Science - Bioinformatics, , language: English, abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time-consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectation. Automated pattern matching- the ability of a program to compare known patterns and determine the degree of similarity forms the basis for automated sequence analysis, modeling of protein structures, locating homologous genes, data mining, Internet search engines etc. in bioinformatics. Data mining relies on algorithm pattern matching to locate patterns in online and local databases, using a variety of technologies, from simple keyword matching to rule based expert system and artificial neural networks. In this dissertation, the basic problems related to pattern reorganization and pattern matching for nucleotide and protein sequence alignment are discussed. The main techniques used to solve these problems and a comprehensive survey of most influential algorithms that were proposed during the last decay is described.
