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Abstract:
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Bioinformatics as a science of the future faces the problems of processing
a large amount of data that is increasing every day. In addition to the problem
of data storage, the challenge is also data analysis and the understanding of hidden
relations between biological entities that are observed only after unifying data
from different data sources. This thesis proposes a novel data model for the unification
of heterogeneous data from multiple bioinformatics databases and a system
architecture design for implementing software systems based on the proposed data
model. Additionally, the thesis defines an automated pipeline for discovering new
semantic similarity relations based on data mining methods using the data found
in the proposed data model. The data model, software architecture, and automatic
pipeline are evaluated using data from five real-world bioinformatics databases. The
results demonstrate a high flexibility of the data model and the high efficiency of
the software system implemented following the proposed architecture design. |