Research And Application Of Bioinformatics Data Fusion And Search Algorithms For Translational Medicine

For the reason that there is no effective association between current basic medical research and clinic treatment, which lead to the latest research results of basic medicine cannot be quickly apply to clinical practice, international medicine proposed the concept of Translational Medicine. However, due to the diversity, complexity and mass of bioinformatics data, the implementation of translational medicine needs to integrate multiple heterogeneous medical databases to process, analyze and integrate data. The main work of this thesis focuses on the analysis and integration of biological data in translational medicine.The thesis first analyzes the characteristics and development trend of translational medicine and bioinformatics databases and proposes the issues need to address such as the data storage, unified annotation and data analysis. Then the thesis presents a unified bioinformatics data model for translational medicine. This model uses an ER-EAV mixture model to solve the problem of massive data kinds and uncertain data attributes in translational research and ensure the query efficiency. Besides, it also employs a unified semantic framework based on Gene Ontology, which is used to solve the semantic heterogeneity between different databases in the integration of translational medicine. Next, on the basis of comparing of several biological database searching algorithms currently used, the thesis proposes a similarity searching algorithm based on suffix tree called ST_Index. This algorithm uses the index table based on suffix tree to speed up the search and ensures search sensitivity through block search. The experiments show that this algorithm is better than BLAST algorithm in sensitivity and efficiency, which provides a fast and efficiency search method for genomics analysis and sequence alignment in translational medicine.Finally, based on the analysis of the translational research through an example the thesis designs a data integration system and its database concept model. Then it analyzes data fusion applications for disease diagnose, data query and biomarker screening. The integration of biological data combines the proposed database searching algorithm as the core of genomics analysis and uses the unified bio-data model to integrate various data of genomics, proteomics, and clinical genomics and so on in translational medicine, which results in a rapid conversion bridge between basic medical research and clinical practice and promote the achievement of translational medicine.