How to use the methods of quantitative analysis to evaluate the importance of nodes or groups in complex networks is one of the important issues to be solved urgently in the field of network science research. Compared with the complex network, in which an edge represents the direct adjacent relationship between two nodes, the unique “hyperedge” in a hypernetwork is more suitable for representing groups, teams, and community structures of multiple nodes. Identifying the importance of teams and communities in the network is more conducive to controlling information dissemination in groups accurately, suppressing community-based outbreaks, predicting team research results, and discovering important drug targets. Existing algorithms focus on identifying important nodes in hypernetworks, and mining for important hyperedges is rare. In this paper, based on the hypergraph theory, combined with the property of the minimum eigenvalue of the grounded Laplacian matrix of the hypernetwork, a new index MEGL is proposed to identify the important hyperedges in hypernetwork. And it is applied in the drug target hypernetwork, which can not only identify important targets, but also identify important drugs. This method has important guiding significance for our drug development and target prediction, and also has certain reference significance for identifying important teams and communities in the network.
KEYWORDS: Data modeling, Proteins, Mathematical modeling, Computer simulations, Monte Carlo methods, Analytical research, Numerical simulations, Data processing
Based on the traditional drug-target interactions, a drug-target hypernetwork evolution model was constructed using hypergraph theory. The evolutionary law of the growth of drug-target interactions was analyzed by mean-field theory, and it was found that the distribution of drug-target hypernetwork conformed to a power-law distribution, and further theoretical analysis obtained that the power exponent of the distribution was correlated with the growth rate of the target species corresponding to drug development. A larger exponent tends to explore new targets. By analyzing the drug target data collected from the drugbank in 2021, it was confirmed that the empirical results are consistent with the theoretical analysis and simulation results.
Pre-trained language model has a good performance in text summarization task, thus we present a neural text summarization based on a powerful pre-trained language model GPT-2. In this paper, we propose a Chinese text summarization model by extending into our downstream task to acquire relevant, contentful, and coherent summarization. By extensive experiments, our model achieves absolute improvements of 10.75% on ROUGE-1, 13.85% on ROUGE-2, and 9.73% on ROUGE-L on the LCSTS datasets. Compared with the state-of-the-art summarization model, e.g. BERTSUM based model, our model also achieves an improvement of 25.22% on ROUGE-1.
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