Genomic Splicing Sites Prediction Algorithm Based on Nucleotide Sequence Pattern

Splicing sites prediction plays a major role in genomic research of biomedical science. If splicing sites of genomic sequence can be correctly predicted, numerous crucial problems in biomedical area may be resolved naturally. Some typical examples are finding pathogenic factors of serious diseases and developing new gene therapy and so on. The major difficulty in developing the method of predicting the splicing sites lies in the diversity of species, which leads to substantially different sequence patterns of splicing sites among various species. For instance, the existing splicing sites prediction algorithms cannot effectively predict the splicing sites of influenza virus. For this reason, we propose a new genomic splicing sites prediction algorithm in this study, which is based on a data mining technology and many biomedical findings, to discover the candidate patterns of splicing sites in the gene sequences, to accurately predict splicing sites in the gene sequences, and to detect many new candidate splicing sites.