Analysis of genetic data via data mining methods and its applications

The prediction of the complete structure of genes is one of the important tasks of bioinformatics, especially in eukaryotes. A crucial part in gene structure prediction is to determine the splice sites in the coding region. Identification of splice sites depends on the precise recognition of the boundaries between exons and introns of a given DNA sequence. This problem can be formulated as a classification of sequence elements into `exon-intron? (EI), `intron-exon? (IE) or `None? (N) boundary classes.In this thesis, we propose a new Weighted Position Specific Scoring Method (WPSSM) to recognize splice sites which uses a position-specific scoring matrix constructed by nucleotide base frequencies. A genetic algorithm is used in order to tune the weight and threshold parameters of the positions on. This method comprises of three phases: learning phase, identification phase and validation phase. In this study, the optimal position weights and threshold parameter are found via genetic algorithm. The proposed WPSS method poses efficient results compared to the performance of various methods proposed in the literature. Computational experiments are conducted on the DNA sequence dataset from `UCI Repository of machine learning databases?.