Analysis of drug–target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.

The gene regulatory networks (GRNs) of immune cells not only indicate cell identity but also reveal the dynamic changes of immune cells when comparing their GRNs. Cancer immunotherapy has advanced in the past few years. Immune-checkpoint blockades (i.e., blocking PD-1, PD-L1, or CTLA-4) have shown durable clinical effects on some patients with various advanced cancers. However, major gaps in our knowledge of immunotherapy have been recognized. To fill these gaps, we conducted a systematic analysis of the GRNs of key immune cell subsets (i.e., B cell, CD4, CD8, CD8 naïve, CD8 Effector memory, CD8 Central Memory, regulatory T, Thelper1, Thelper2, Thelp17, and NK (Nature killer) and DC (Dendritic cell) cells associated with cancer immunologic therapies. We showed that most of the GRNs of these cells in blood share key important hub regulators, but their subnetworks for controlling cell type-specific receptors are different, suggesting that transformation between these immune cell subsets could be fast so that they can rapidly respond to environmental cues. To understand how cancer cells send molecular signals to immune cells to make them more cancer-cell friendly, we compared the GRNs of the tumor-infiltrating immune T cells and their corresponding immune cells in blood. We showed that the network size of the tumor-infiltrating immune T cells’ GRNs was reduced when compared to the GRNs of their corresponding immune cells in blood. These results suggest that the shutting down certain cellular activities of the immune cells by cancer cells is one of the key molecular mechanisms for helping cancer cells to escape the defense of the host immune system. These results highlight the possibility of genetic engineering of T cells for turning on the identified subnetworks that have been shut down by cancer cells to combat tumors.

Introduction: This meta-analysis aimed to compare the differences in postoperative efficacy between oblique lumbar interbody fusion (OLIF) and transforaminal lumbar interbody fusion (TLIF) in the treatment of lumbar degenerative diseases. Materials and methods: Strictly based on the search strategy, we searched the published papers on OLIF and TLIF for the treatment of lumbar degenerative diseases in PubMed, Embase, CINAHL, and Cochrane Library. A total of 607 related papers were retrieved, and 15 articles were finally included. The quality of the papers was evaluated according to the Cochrane systematic review methodology, and the data were extracted and meta-analyzed using Review manager 5.4 software. Results: Through comparison, it was found that in the treatment of lumbar degenerative diseases, the OLIF group had certain advantages over the TLIF group in terms of intraoperative blood loss, hospital stay, visual analog scale (VAS) for leg pain (VAS-LP), Oswestry disability index (ODI), disc height (DH), foraminal height (FH), fused segmental lordosis (FSL), and cage height, and the differences were statistically significant. The results were similar in terms of surgery time, complications, fusion rate, VAS for back pain (VAS-BP) and various sagittal imaging indicators, and there was no significant difference. Conclusions: OLIF and TLIF can relieve low back pain symptoms in the treatment of lumbar degenerative diseases, but OLIF has certain advantages in terms of ODI and VAS-LP. In addition, OLIF has the advantages of minor intraoperative trauma and quick postoperative recovery.

Background: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA–disease associations. Results: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA–disease associations (ADPMDA). We first construct the miRNA–disease heterogeneous graph based on known miRNA–disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA–disease pairs. Conclusion: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA–disease associations.

In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, data heterogeneity and random noise pose significant difficulties for scRNA-seq data analysis. We have proposed an adversarial dense graph convolutional network architecture for single-cell classification. Specifically, to enhance the representation of higher-order features and the organic combination between features, dense connectivity mechanism and attention-based feature aggregation are introduced for feature learning in convolutional neural networks. To preserve the features of the original data, we use a feature reconstruction module to assist the goal of single-cell classification. In addition, HNNVAT uses virtual adversarial training to improve the generalization and robustness. Experimental results show that our model outperforms the existing classical methods in terms of classification accuracy on benchmark datasets. The source code of HNNVAT is available at https://github.com/DisscLab/HNNVAT. Supplementary data are available at Bioinformatics online.