Application of connectivity index of cubic fuzzy graphs for identification of danger zones of tsunami threat.

Fuzzy graphs are very important when we are trying to understand and study complex systems with uncertain and not exact information. Among different types of fuzzy graphs, cubic fuzzy graphs are special due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. To figure out how things are connected in cubic fuzzy graphs, we need to know about cubic α-strong, cubic β-strong and cubic δ-weak edges.

PmiRtarbase: A positive miRNA-target regulations database.

MicroRNAs (miRNAs) are ~22 nt small non-coding RNA segments that are widely involved in the regulation of gene expression. Accumulating evidences show that miRNAs not only inhibit the expression of some targeted genes but also promote that of some targeted genes in specific conditions. Over the past decades, many miRNA-target databases have been developed from computational prediction and/or experimental validation perspectives.

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.

Background: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.

Predicting Cross-Species Infection of Swine Influenza Virus with Representation Learning of Amino Acid Features.

Swine influenza viruses (SIVs) can unforeseeably cross the species barriers and directly infect humans, which pose huge challenges for public health and trigger pandemic risk at irregular intervals. Computational tools are needed to predict infection phenotype and early pandemic risk of SIVs. For this purpose, we propose a feature representation algorithm to predict cross-species infection of SIVs.