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In this paper, we propose and develop a multi-visual-concept ranking (MultiVCRank) scheme for image retrieval. The key idea is that an image can be represented by several visual concepts, and a hypergraph is built based on visual concepts as hyperedges, where each edge contains images as vertices to share a specific visual concept. In the constructed hypergraph, the weight between two vertices in a hyperedge is incorporated, and it can be measured by their affinity in the corresponding visual concept. A ranking scheme is designed to compute the association scores of images and the relevance scores of visual concepts by employing input query vectors to handle image retrieval. In the scheme, the association and relevance scores are determined by an iterative method to solve limiting probabilities of a multi-dimensional Markov chain arising from the constructed hypergraph. The convergence analysis of the iteration method is studied and analyzed. Moreover, a learning algorithm is also proposed to set the parameters in the scheme, which makes it simple to use. Experimental results on the MSRC, Corel, and Caltech256 data sets have demonstrated the effectiveness of the proposed method. In the comparison, we find that the retrieval performance of MultiVCRank is substantially better than those of HypergraphRank, ManifoldRank, TOPHITS, and RankSVM.

Background: The use of multiple drugs at the same time can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. But it is difficult to test the drug-drug interaction widely and effectively before the drug is put into market. Therefore, the prediction of drug-drug interaction has become an important research in biomedical field.Results: In recent years, researchers have used deep learning to predict drug-drug interaction by using drug structural features and graph theory, and they have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper. The structural features of drugs are constructed by using SMILES data. The topological features of drugs in knowledge graph are obtained by graph neural network. The structural and topological features of drugs are aggregated to predict the interaction of new drug pairs. Conclusions: The experimental results show that the model proposed in this paper combines a variety of data sources, and has better prediction performance compared with the existing prediction model of drug-drug interaction prediction. The most striking result is that five out of top ten predicted new interaction of drugs are verified from the latest database, which proves the credibility of SmileGNN.

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