Background
Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.
Methods
First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.
Results and conclusions
We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.
S/Zn codoped TiO2nanomaterials were synthesized by a sol-gel method. X-ray diffraction, UV-vis diffuse reflectance spectroscopy, transmission electron microscopy, photoluminescence spectroscopy, and X-ray photoelectron spectroscopy were used to characterize the morphology, structure, and optical properties of the prepared samples. The introduction of Zn and S resulted in significant red shift of absorption edge for TiO2-based nanomaterials. The photocatalytic activity was evaluated by degrading reactive brilliant red X-3B solution under simulated sunlight irradiation. The results showed S/Zn codoped TiO2exhibited higher photocatalytic activity than pure TiO2and commercial P25, due to the photosynergistic effect of obvious visible light absorption, efficient separation of photoinduced charge carriers, and large surface area. Moreover, the content of Zn and S in the composites played important roles in photocatalytic activity of TiO2-based nanomaterials.
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