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 ß-strong and cubic δ-weak edges. These concepts better help in making decisions, solving problems and analyzing things like transportation, social networks and communication systems. The applicability of connectivity and comprehension of cubic fuzzy graphs have urged us to discuss connectivity in the domain of cubic fuzzy graphs. In this paper, the terms partial cubic α-strong and partial cubic δ-weak edges are introduced for cubic fuzzy graphs. The bounds and exact expression of connectivity index for several cubic fuzzy graphs are estimated. The average connectivity index for cubic fuzzy graphs is also defined and some results pertaining to these concepts are proved in this paper. The results demonstrate that removing some vertices or edges may cause a change in the value of connectivity index or average connectivity index, but the change will not necessarily be related to both values. This paper also defines the concepts of partial cubic connectivity enhancing node and partial cubic connectivity reducing node and some related results are proved. Furthermore, the concepts of cubic α-strong, cubic ß- strong, cubic δ-weak edge, partial cubic α-strong and partial cubic δ-weak edges are utilized to identify areas most affected by a tsunami resulting from an earthquake. Finally, the research findings are compared with the existing methods to demonstrate their suitability and creativity.

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. However, there is no database available to systematically collect positive miRNA-target associations that are essential in deciphering the miRNA regulation mechanism. To promote the miRNA study, we developed a new database: PmiRtarbase that acquires validated positive miRNA-target interactions by mining published literature. It includes 312 curated associations between 119 miRNAs and 169 genes in 8 species from 130 studies and summarizes the conditions and detailed descriptions of the miRNA-target associations. We also constructed a database named PmiRtarbase, a user-friendly interface to conveniently search and download all related entries. This elaborate database aims to serve as a beneficial resource for studying the miRNA positive regulation mechanism and miRNA-based therapeutics. DATA AVAILABILITY: The full positive miRNA-target data can be accessed through the link http://www.lwb-lab.cn/PmiRtarbase. Users of this dataset should acknowledge the contributions of the original authors and properly cite this article.

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. METHODS: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. RESULTS: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. CONCLUSIONS: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

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. We built a high-quality dataset of 1902 viruses. A feature representation learning scheme was applied to learn feature representations from 64 well-trained random forest models with multiple feature descriptors of mutant amino acid in the viral proteins, including compositional information, position-specific information, and physicochemical properties. Class and probabilistic information were integrated into the feature representations, and redundant features were removed by feature space optimization. High performance was achieved using 20 informative features and 22 probabilistic information. The proposed method will facilitate SIV characterization of transmission phenotype.