Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.
We study the propagation of pollutants emitted from a single generator such as a factory chimney located between 2 mountains as well as its effects on an observed area such as a village or agricultural land. The problem is formulated as a system of partial differential equations, composed of Navier-Stokes equations and an advection-diffusion equation, and is solved by the finite element method. We visualize the propagation of the pollutants for several variants of the problem depending on the heights of the mountains and investigate their negative effects on the observed area by computing an average concentration of the pollutants over the observed area. We found that the observed area between the two mountains experienced a long-term negative effect compared with those located on flat land. This is because the mountain on the side, where the wind is blowing, obstructs the wind resulting in air recirculation. In contrast, the other mountain bounces some pollutants back to the observed area, preventing them from leaving the domain. The higher the mountains, the longer the time the pollutants remain in the observed area. If the heights of the mountains encircling the observed area are not equal, the residual remains in the area longer if the taller mountain is on the windward side. HIGHLIGHTS Air Visualization of air pollution between two mountains Air pollution propagation modeling A system of partial differential equations for air pollution modeling with FEM GRAPHICAL ABSTRACT
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