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Drug–target interactions (DTIs) prediction research presents important significance for promoting the development of modern medicine and pharmacology. Traditional biochemical experiments for DTIs prediction confront the challenges including long time period, high cost and high failure rate, and finally leading to a low-drug productivity. Chemogenomic-based computational methods can realize high-throughput prediction. In this study, we develop a deep collaborative filtering prediction model with multiembeddings, named DCFME (deep collaborative filtering prediction model with multiembeddings), which can jointly utilize multiple feature information from multiembeddings. Two different representation learning algorithms are first employed to extract heterogeneous network features. DCFME uses the generated low-dimensional dense vectors as input, and then simulates the drug–target relationship from the perspective of both couplings and heterogeneity. In addition, the model employs focal loss that concentrates the loss on sparse and hard samples in the training process. Comparative experiments with five baseline methods show that DCFME achieves more significant performance improvement on sparse datasets. Moreover, the model has better robustness and generalization capacity under several harder prediction scenarios. [ABSTRACT FROM AUTHOR]

The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases and the search for therapeutic drugs. However, the increase in data also increases the difficulty of biological networks analysis. Therefore, algorithms that can handle large, heterogeneous and complex data are needed to better analyze the data of these network structures and mine their useful information. Deep learning is a branch of machine learning that extracts more abstract features from a larger set of training data. Through the establishment of an artificial neural network with a network hierarchy structure, deep learning can extract and screen the input information layer by layer and has representation learning ability. The improved deep learning algorithm can be used to process complex and heterogeneous graph data structures and is increasingly being applied to the mining of network data information. In this paper, we first introduce the used network data deep learning models. After words, we summarize the application of deep learning on biological networks. Finally, we discuss the future development prospects of this field. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is reported to associate with abnormal expression of SCF E3 ubiquitin ligases. FBXW10, an F-box protein of the E3 ubiquitin ligases, was abnormally regulated in HCC patients. However, whether FBXW10 is associated with HCC has not yet been evaluated. Here, we analyzed the associations between overall survival and various risk factors in 191 HCC tissues. Univariate and multivariate analyses demonstrated that FBXW10 was an independent risk factor related to HCC prognosis. The results showed that FBXW10, gender and tumor state were strongly associated with overall survival in HCC patients. Furthermore, high expression of FBXW10 was associated with poor survival among male HCC patients but not female HCC patients. FBXW10 was more highly expressed in male HCC tissues and more strongly related to vascular invasion in male HCC patients. Consistent with these findings, the male FBXW10-Tg(+) mice were more susceptible to tumorigenesis, changes in regenerative capacity, and liver injury and inflammation but not changes in liver function than FBXW10-Tg(–) mice. FBXW10 promoted cell proliferation and migration in HCC cell lines. Our findings reveal that FBXW10, an independent risk factor for HCC, promotes hepatocarcinogenesis in male patients, and is also a potential prognostic marker in male patients with HCC. [ABSTRACT FROM AUTHOR]

The article presents a study on the effects of Camellia assamica variant Kucha tea extract in mice. The study showed that the administration of Kucha in mice had helped reduce the postprandial blood glucose levels after sucrose and soluble starch loading. It concludes that Kucha's hypoglycemic effects may be due to inhibition of disaccharides activities.