LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble.

Introduction: Numerous studies show that microbes in the human body are very closely linked to the human host and can affect the human host by modulating the efficacy and toxicity of drugs. However, discovering potential microbe-drug associations through traditional wet labs is expensive and time-consuming, hence, it is important and necessary to develop effective computational models to detect possible microbe-drug associations. Methods: In this manuscript, we proposed a new prediction model named LCASPMDA by combining the learnable graph convolutional attention network and the self-paced iterative sampling ensemble strategy to infer latent microbe-drug associations. In LCASPMDA, we first constructed a heterogeneous network based on newly downloaded known microbe-drug associations. Then, we adopted the learnable graph convolutional attention network to learn the hidden features of nodes in the heterogeneous network. After that, we utilized the self-paced iterative sampling ensemble strategy to select the most informative negative samples to train the Multi-Layer Perceptron classifier and put the newly-extracted hidden features into the trained MLP classifier to infer possible microbe-drug associations. Results and discussion: Intensive experimental results on two different public databases including the MDAD and the aBiofilm showed that LCASPMDA could achieve better performance than state-of-the-art baseline methods in microbe-drug association prediction.

Exploring the mechanism of daphne-type diterpenes against gastric cancer cells.

Gastric cancer is one of the common malignant tumors. It is reported that daphne-type diterpenes have inhibitory effects on gastric cancer cells, but the mechanism is still unknown. To explore the detailed mechanism of the anticancer effect of daphne-type diterpenes, we carried out an integrated network pharmacology prediction study and selected an effective component (yuanhuacine, YHC) for the following validation in silico and in vitro. The result showed that daphne-type diterpenes exerted an anti-tumor effect by targeting proto-oncogene tyrosine-protein kinase SRC as well as regulating the Ras/MAPK signaling pathway, which caused the apoptosis and mitochondrial damage in gastric cancer cells.

MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec.

Introduction: Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes. Methods: In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions. Results and discussion: Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.