Abstract: Si-Wu-Tang (SWT), a traditional Chinese herbal formula, has shown an effect on antibreast cancer. However, the mechanisms and bioactive components of SWT are still unclear. Fortunately, cell type-specific molecular network has provided an effective method. This study integrated the data of formula components, all types of biomolecules in the human body, and nonexpressed protein in breast cancer cells and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). Biological network showed 59 bioactive components acting on 15 breast cancer-related targets. The antibreast cancer mechanisms were summarized by enrichment analysis: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. In addition, we discovered that verbascoside played the role of antibreast cancer by inhibiting cell proliferation, but there was not a report on this effect. The results of CCK8 and western blot were consistent with the antibreast cancer effect of verbascoside based on biological network. Biological network modeling by EGS and network analysis provide an effective way for uncovering the mechanism and identifying the bioactive components of SWT.

Abstract: We employed an integrative strategy to present subtractive and comparative metabolic and genomic-based findings of therapeutic targets against Streptococcus parauberis. For the first time, we not only identified potential targets based on genomic and proteomic database analyses but also recommend a new antimicrobial drug for the treatment of olive flounder (Paralichthys olivaceus) infected with S. parauberis. To do that, 102 total annotated metabolic pathways of this bacterial strain were extracted from computational comparative metabolic and genomic databases. Six druggable proteins were identified from these metabolic pathways from the DrugBank database with their respective genes as mtnN, penA, pbp2, murB, murA, coaA, and fni out of 112 essential nonhomologous proteins. Among these hits, 26 transmembrane proteins and 77 cytoplasmic proteins were extracted as potential vaccines and drug targets, respectively. From the FDA DrugBank, ceftiofur was selected to prevent antibiotic resistance as it inhibited our selected identified target. Florfenicol is used for treatment of S. parauberis infection in flounder and was chosen as a comparator drug. All tested strains of fish isolates with S. parauberis were susceptible to ceftiofur and florfenicol with minimum inhibitory concentrations (MIC) of 0.0039-1 mug/mL and 0.5-8 mug/mL, IC50 of 0.001-0.5 mug/mL and 0.7-2.7 mug/mL, and minimum biofilm eradication concentrations (MBEC) of 2-256 mug/mL and 4-64 mug/mL, respectively. Similar susceptibility profiles for ceftiofur and florfenicol were found, with ceftiofur observed as an effective and potent antimicrobial drug against both planktonic and biofilm-forming strains of the fish pathogen Streptococcus parauberis, and it can be applied in the aquaculture industry. Thus, our predictive approach not only showed novel therapeutic agents but also indicated that marketed drugs should also be tested for efficacy against newly identified targets of this important fish pathogen.

Abstract: Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb-ingredient-target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Abstract: A novel coronavirus (SARS-CoV-2, formerly known as nCoV-2019) that causes an acute respiratory disease has emerged in Wuhan, China and spread globally in early 2020. On January the 30th, the World Health Organization (WHO) declared spread of this virus as an epidemic and a public health emergency. With its highly contagious characteristic and long incubation time, confinement of SARS-CoV-2 requires drastic lock-down measures to be taken and therefore early diagnosis is crucial. We analysed transcriptome of SARS-CoV-2 infected human lung epithelial cells, compared it with mock-infected cells, used network-based reporter metabolite approach and integrated the transcriptome data with protein-protein interaction network to elucidate the early cellular response. Significantly affected metabolites have the potential to be used in diagnostics while pathways of protein clusters have the potential to be used as targets for supportive or novel therapeutic approaches. Our results are in accordance with the literature on response of IL6 family of cytokines and their importance, in addition, we find that matrix metalloproteinase 2 (MMP2) and matrix metalloproteinase 9 (MMP9) with keratan sulfate synthesis pathway may play a key role in the infection. We hypothesize that MMP9 inhibitors have potential to prevent "cytokine storm" in severely affected patients.

Abstract: Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

Abstract: Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.

Abstract: All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.

Abstract: Acute respiratory distress syndrome (ARDS) is a complex cascade that develops from acute lung injury (ALI). Ginseng can be used to treat ALI/ARDS. Studies have shown that some of ingredients in ginseng had anti-inflammation, antioxidative, and immune regulation effects and can protect alveolar epithelial cells in mice. However, the potential targets, biological processes, and pathways related to ginseng against ALI/ARDS have not been investigated systematically. We employed network pharmacology, molecular docking, and animal experiments to explore the therapeutic effects and underlying mechanism of action of ginseng against ALI/ARDS. We identified 25 compounds using ultrahigh-performance liquid chromatography Q-Orbitrap mass spectrometry and their 410 putative targets through database analyses. Sixty-nine of them were considered to be key targets of ginseng against ALI/ARDS according to overlapping with ALI/ARDS-related targets and further screening in a protein-protein interaction (PPI) network. The phosphatidylinositol 3-kinase-protein kinase B (PI3K-AkT) and mitogen-activated protein kinase (MAPK) pathways were recognized to have critical roles for ginseng in ALI/ARDS treatment. Signal transducer and activator of transcription (STAT) 3, vascular endothelial growth factor A (VEGFA), fibroblast growth factor (FGF) 2, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), MAPK1, and interleukin (IL) 2 were the top six nodes identified by analyses of a compound-target-pathway network. Molecular docking showed that most of the ingredients in ginseng could combine well with the six nodes. Ginseng could reduce the pathologic damage, neutrophil aggregation, proinflammatory factors, and pulmonary edema in vivo and inhibit the PI3K-Akt signaling pathway and MAPK signaling pathway through downregulating expressions of STAT3, VEGFA, FGF2, PIK3CA, MAPK1, and IL2. Our study provides a theoretical basis for ginseng treatment of ALI/ARDS.

Abstract: Identification of the toxicity of compounds is more crucial before entering clinical trials. Awareness of physiochemical properties, possible targets and side effects has become a major public health issue to reduce risks. Experimental determination of analyzing the physiochemical properties of a drug, their interaction with specific receptors and identifying their side-effects remain challenging is time consuming and costly. We describe a manually compiled database named DaiCee database, which contains 2100 anticancer drugs with information on their physiochemical properties, targets of action and side effects. It includes both synthetic and herbal anti-cancer compounds. It allows the search for SMILES notation, Lipinski's and ADME/T properties, targets and side effect profiles of the drugs. This helps to identify drugs with effective anticancer properties, their toxic nature, drug-likeness for in-vitro and in-vivo experiments. It also used for comparative analysis and screening of effective anticancer drugs using available data for compounds in the database. The database will be updated regularly to provide the users with latest information. The database is available at the URL http://www.hccbif.org/usersearch.php.

Abstract: Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.