We present the metapopulation dynamic model for epidemic spreading of random walkers between subpopulations. A subpopulation is represented by a node on a graph. Each agent or individual is either susceptible (S) or infected (I). All agents move by random walk on the graph; namely, each agent randomly determines the destination of migration. The reaction-diffusion equations are presented as ordinary differential equations, not partial differential equations. To evaluate the risk of each subpopulation (node), we obtain the solutions of reaction-diffusion equations analytically and numerically for small, complete, cycle and star graphs. If a graph is homogeneous, or if every node has the same degree, then the solution never changes for any nodes. However, when a graph is heterogeneous, the infection density in equilibrium differs entirely among nodes. For example, on star graphs, the hub seems to be a supply source of disease because the infection density at the hub is much higher than that at the other nodes. On every graph, the epidemic thresholds are identical for all nodes.
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http://dx.doi.org/10.1016/j.jtbi.2018.04.029 | DOI Listing |
Sci Rep
January 2025
Faculty of Education and Arts, Australian Catholic University, Sydney, NSW, 2118, Australia.
Every node in a network is said to be resolved if it can be uniquely identified by a vector of distances to a specific set of nodes. The metric dimension is equivalent to the least possible cardinal number of a resolving set. Conditional resolving sets are obtained by imposing various constraints on resolving set.
View Article and Find Full Text PDFBiometrics
October 2024
Department of Urology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai 200092, China.
Numerous statistical methods have been developed to search for genomic markers associated with the development, progression, and response to treatment of complex diseases. Among them, feature ranking plays a vital role due to its intuitive formulation and computational efficiency. However, most of the existing methods are based on the marginal importance of molecular predictors and share the limitation that the dependence (network) structures among predictors are not well accommodated, where a disease phenotype usually reflects various biological processes that interact in a complex network.
View Article and Find Full Text PDFNeural Netw
December 2024
College of Science, Shantou University, Shantou 515063, China. Electronic address:
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two.
View Article and Find Full Text PDFBrief Bioinform
November 2024
College of Computing & Data Science, Nanyang Technological University, 639798, Singapore.
Motivation: Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios.
View Article and Find Full Text PDFNAR Genom Bioinform
December 2024
Computational Biochemistry Laboratory, Department of Chemistry and Centre for Advanced Studies in Chemistry, Panjab University, Sector 14, Chandigarh 160014, India.
Water is essential for the formation, stability and function of RNA-protein complexes. To delineate the structural role of water molecules in shaping the interactions between RNA and proteins, we comprehensively analyzed a dataset of 329 crystal structures of these complexes to identify water-mediated hydrogen-bonded contacts at RNA-protein interface. Our survey identified a total of 4963 water bridges.
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