A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.

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%.

Performance of fish sludge solubilization and phototrophic bioconversion by purple phototrophic bacteria for nutrient recovery in aquaponic system.

Nutrient recovery from fish sludge in aquaponics is crucial to improve the economic output of a system sustainably and hygienically. Currently, fish sludge is treated using conventional anaerobic and aerobic mineralization, which does not allow the recovery of valuable nutrients in fish wastes. In this study, a two-stage approach (named as solubilization process and phototrophic bioconversion) is proposed to convert fish sludge into mineral nutrients and biomass nutrients using purple phototrophic bacteria (PPB), thereby promoting the growth of plants and fish simultaneously in aquaponics. Anaerobic and aerobic solubilization methods are tested to pretreat the fish sludge, generating substrates for PPB. Anaerobic solubilization yields 2.1 times more soluble chemical oxygen demand (SCOD) and 3.7 times more total volatile fatty acid (t-VFA) from fish sludge compared with aerobic solubilization. The anaerobic solubilization effluent indicates a CODt-VFA/SCOD of 60% and a VFA comprising 13.3% acetate and 49.0% propionate for PPB. The phototrophic bioconversion using anaerobic solubilization effluent under the light-anaerobic condition results in the highest biomass yield (0.94 g CODbiomass/g CODremoved) and the highest PPB dominance (Ectothiorhodospira, 58.7%). The anaerobic solubilization and light-anaerobic phototrophic bioconversion achieves 54.1% of carbon recovery efficiency (CRE) (in terms of COD), as well as 44.8% and 91.3% of nutrient recovery efficiency (NRE) for N and P. A novel multiloop aquaponic system combined with PPB-based nutrient recovery is proposed for the reuse of mineral nutrients and PPB biomass generated from fish sludge.