An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

Network Pharmacology to Uncover the Biological Basis of Spleen Qi Deficiency Syndrome and Herbal Treatment

Spleen qi deficiency (SQD) syndrome is one of the basic traditional Chinese medicine (TCM) syndromes related to various diseases including chronic inflammation and hypertension and guides the use of many herbal formulae. However, the biological basis of SQD syndrome has not been clearly elucidated due to the lack of appropriate methodologies. Here, we propose a network pharmacology strategy integrating computational, clinical, and experimental investigation to study the biological basis of SQD syndrome. From computational aspects, we used a powerful disease gene prediction algorithm to predict the SQD syndrome biomolecular network which is significantly enriched in biological functions including immune regulation, oxidative stress, and lipid metabolism. From clinical aspects, SQD syndrome is involved in both the local and holistic disorders, that is, the digestive diseases and the whole body’s dysfunctions. We, respectively, investigate SQD syndrome-related digestive diseases including chronic gastritis and irritable bowel syndrome and the whole body’s dysfunctions such as chronic fatigue syndrome and hypertension. We found innate immune and oxidative stress modules of SQD syndrome biomolecular network dysfunction in chronic gastritis patients and irritable bowel syndrome patients. Lymphocyte modules were downregulated in chronic fatigue syndrome patients and hypertension patients. From experimental aspects, network pharmacology analysis suggested that targets of Radix Astragali and other four herbs commonly used for SQD syndrome are significantly enriched in the SQD syndrome biomolecular network. Experiments further validated that Radix Astragali ingredients promoted immune modules such as macrophage proliferation and lymphocyte proliferation. These findings indicate that the biological basis of SQD syndrome is closely related to insufficient immune response including decreased macrophage activity and reduced lymphocyte proliferation. This study not only demonstrates the potential biological basis of SQD syndrome but also provides a novel strategy for exploring relevant molecular mechanisms of disease-syndrome-herb from the network pharmacology perspective.

Uncovering complex molecular networks in host–pathogen interactions using systems biology

Interactions between pathogens and their hosts can induce complex changes in both host and pathogen states to privilege pathogen survival or host clearance of the pathogen. To determine the consequences of specific host–pathogen interactions, a variety of techniques in microbiology, cell biology, and immunology are available to researchers. Systems biology that enables unbiased measurements of transcriptomes, proteomes, and other biomolecules has become increasingly common in the study of host–pathogen interactions. These approaches can be used to generate novel hypotheses or to characterize the effects of particular perturbations across an entire biomolecular network. With proper experimental design and complementary data analysis tools, high-throughput omics techniques can provide novel insights into the mechanisms that underlie processes from phagocytosis to pathogen immune evasion. Here, we provide an overview of the suite of biochemical approaches for high-throughput analyses of host–pathogen interactions, analytical frameworks for understanding the resulting datasets, and a vision for the future of this exciting field.