Probabilistic Approach Processing Scheme Based on BLAST for Improving Search Speed of Bioinformatics

摘要

As researchers on bioinformatics using heuristic algorithms have been increasingly studied, information management used in various bioinformatics fields (new drug development, medical diagnosis, agricultural product improvement, etc.) has been studied mainly on BLAST algorithm. However, many of the algorithms that are being used in the large genome database use a complete sorting procedure, which takes a lot of time to search the database for proteins or nucleic acid sequences, which causes many problems in processing large amounts of bio information. We propose a BLAST-based probabilistic access processing method that can manage, analyze and process a large amount of bio data distributed based on information communication infrastructure and IT technology. The proposed method aims to improve the accessibility of data by linking weighted bioinformatics information with probability factors to easily access large capacity bio data. In addition, the proposed scheme classifies the priority information allocated to the bioinformatics information by hierarchical grouping according to the degree of similarity, thereby ensuring high accuracy of the search results of the bioinformatics information, and at the same time, the goal is to obtain low processing time by classifying information (type, attribute, priority, etc.) into weights by property. Previous researchers have suggested clustering algorithms for fragmentation of genetic information to solve the problem of haplotype assembly in genetics, or proposed particle swarm optimization methods similar to existing genetic algorithms using heuristic clustering method based on MEC model. In the performance evaluation, the proposed method improved the accuracy by average 13.5% and the efficiency of the data retrieval by average 19.7% more than previous scheme. The overhead of Bioinformatics information processing was 8.8% lower and the processing time was average 13.5% lower.