The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.
Gene Ontology ; Microarray Analysis ; Protein Interaction Maps

No abstract available.
Computational Biology* ; Genomics* ; Protein Interaction Maps

Atherosclerosis is a complex disease characterized by lipid accumulation in the vascular wall and influenced by multiple genetic and environmental factors. To understand the mechanisms of molecular regulation related to atherosclerosis better, a protein interaction network was constructed in the present study. Genes were collected in nucleotide database and interactions were downloaded from Biomolecular Object Network Database (BOND). The interactional data were imported into the software Cytoscape to construct the interaction network, and then the degree characteristics of the network were analyzed for Hub proteins. Statistical significance pathways and diseases were figured out by inputting Hub proteins to KOBAS2. 0. The complete pathway network related to atherosclerosis was constructed. The results identified a series of key genes related to atherosclerosis, which would be the potential promising drug targets for effective prevention.
Atherosclerosis ; genetics ; Databases, Factual ; Humans ; Protein Interaction Mapping ; methods ; Protein Interaction Maps ; Software

Proteins are the final executive actor of cell viability and function. Protein-protein interactions determine the complexity of the organism. Research on the protein interactions can help us understand the function of the protein at the molecular level, learn the cell growth, development, differentiation, apoptosis and understand biological regulation mechanisms and other activities. They are essential for understanding the pathologies of diseases and helpful in the prevention and treatment of diseases, as well as in the development of new drugs. In this paper, we employ the single decision-tree classification model to predict protein-protein interactions in the yeast. The original data came from the existing literature. Using software Clementine, this paper analyzes how these attributes affect the accuracy of the model by adjusting the predicted attributes. The result shows that a single decision tree is a good classification model and it has higher accuracy compared to those in the previous researches.
Algorithms ; Decision Trees ; Fungal Proteins ; chemistry ; Models, Theoretical ; Protein Interaction Domains and Motifs ; Protein Interaction Maps

Animals ; Databases, Protein ; Humans ; Models, Molecular ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping ; methods ; Protein Interaction Maps ; Proteins ; chemistry ; metabolism ; Proteome ; chemistry ; metabolism

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.
Algorithms ; Artificial Intelligence ; Humans ; Models, Biological ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping ; Protein Interaction Maps ; Proteome ; genetics ; metabolism ; Proteomics ; Systems Biology

Danhong injection is a compound preparation of traditional Chinese medicine Salvia miltiorrhiza and Carthamus tinctorius, and has been widely applied in treating coronary heart diseases and ischemic encephalopathy in clinic. Despite the complexity of its chemical compounds and the diversity of targets, especially in system biology, there have not a report for its action mechanism as a whole regulatory biological network. In this study, protein data of S. miltiorrhiza and C. tinctorius were searched in TCMGeneDIT database and agilent literature search (ALS) system to establish the multi-component protein network of S. miltiorrhiza, C. tinctorius and Danhong injection. Besides, the protein interaction network was built based on the protein-protein interaction in Genecards, BIND, BioGRID, IntAct, MINT and other databases. According to the findings, 10 compounds of S. miltiorrhiza and 14 compounds of C. tinctorius were correlated with proteins. The 24 common compounds had interactions with 81 proteins, and formed a protein interaction network with 60 none-isolated nodes. The Cluster ONE module was applied to make an enrichment analysis on the protein interaction network and extract one sub-network with significant difference P <0.05. The sub-network contains 23 key proteins, which involved five signaling pathways, namely Nod-like receptor signaling pathway, epithelial cell signaling in helicobacter pylori infection, Toll-like receptor signaling pathway, RIG-I-like receptor signaling pathway and neurotrophin signaling pathway through KEGG signaling pathway mapping. In this study, the computational system biology approach was adopted to preliminarily explain the molecular mechanism of main compounds of Danhong injection in preventing and treating diseases and provide reference for systematic studies on traditional Chinese medicine compounds.
Computational Biology ; Drugs, Chinese Herbal ; pharmacology ; Injections ; Protein Interaction Maps ; Signal Transduction

To probe the potential hepatotoxic components of Epimedii Folium and investigate its mechanism based on network toxicology and cell experimental validation. According to the previous results of component measurement and cytotoxicity evaluation, 11 active compounds related to hepatotoxicity in Epimedii Folium were chosen as research object in this study. Through SwissTargetPrediction database and GeneCards database, the potentially hepatotoxic targets of Epimedii Folium were obtained. Subsequently, the protein-target interaction network and active compounds-hepatotoxic targets network were established to analyze the core targets and screen the key hepatotoxic compounds in Epimedii Folium. Meanwhile, the signaling pathways and molecular mechanisms were inferred with GO functional enrichment analysis and KEGG pathway enrichment analysis on the core targets. At last, the effect of icaritin as the chief hepatotoxic compound on the indexes related to hepatotoxicity in HL-7702 cells and HepG2 cells was investigated to validate the hepatotoxicity mechanism of Epimedii Folium. Through the network toxicology analysis, 190 action targets and 991 hepatotoxic targets were collected, then 64 potentially hepatotoxic targets of Epimedii Folium including AKT1, EGFR, MAPK3, TNF and so on were obtained, and icaritin was screened as the key hepatotoxic compound. GO functional enrichment analysis indicated 160 biological process terms such as protein phosphorylation and negative regulation of apoptotic process, 41 molecular function terms such as protein binding and ATP binding, and 32 cellular component terms such as cytosol and cell surface. KEGG pathway enrichment analysis inferred 75 signaling pathways involving PI3 K-Akt and HIF-1. After comprehensive analysis, it was inferred that the hepatotoxicity mechanism of Epimedii Folium was related with regulating oxidative stress and apoptosis. The results of cell biology experiments showed that icaritin could significantly increase the level of aspartate aminotransferase and lactate dehydrogenase, reduce the level of glutathione, improve the quality of reactive oxygen species and reduce mitochondrial membrane potential, indicating that it could cause hepatotoxicity by destroying cell membrane structure, inhibiting antioxidant enzyme activity, activating oxidative stress and inducing apoptosis. These results proved the reliability of results of network pharmacology. This study preliminarily clarified the material base and the mechanism of potential hepatotoxicity of Epimedii Folium, which provided important information for further research and safe application.
Drugs, Chinese Herbal/toxicity* ; Plant Leaves ; Protein Interaction Maps ; Reproducibility of Results

The traditional Chinese medicines(TCM) for activating blood circulation and the TCM for regulating Qi are often used in combination in clinical practice. However, their mechanisms are still unclear. The activity spectrum of targets can fuse the active components, targets and intensity of action, which provides support for the discussion of efficacy targets. The chemical components of common TCM sets for activating blood circulation and regulating Qi, as well as the negative sets not for activating blood circulation and re-gulating Qi were obtained from the database of TCM. By the similarity analysis of chemical components in TCM for activating blood circulation and DrugBank database, the predicted targets of chemical components in TCM for activating blood circulation were obtained, and the similarity value of the two was taken as the activity value of the active components and predicted targets. Then, the component-target activity value was weighted. The activity values of herb acting on the same target were fused to construct activity spectra of targets of the herbs for activating blood circulation, herbs for regulating Qi and negative herbs. The targets whose activity values of activating blood circulation and regulating Qi were higher than those of negative herbs were selected as potential targets of efficacy. Protein-protein interaction networks were constructed for topological, GO and KEGG enrichment analysis to determine the key targets of efficacy of activating blood circulation and regulating Qi. The component-target activity information collected from DrugBank database contained 4 499 compounds, 627 targets and 11 295 action relationships. The activating blood function protein-protein interaction network contained 206 nodes and 1 728 edges, while the regulating Qi function protein-protein interaction network contained 230 nodes and 986 edges. The enrichment analysis of topology, GO and KEGG showed that TCM for activating blood circulation mainly exerted its anti-inflammatory, neuroprotective and angiogenic effects on signaling cascade pathway mediated by VEGF/VEGFR2, ERK signaling pathway, calcium signaling pathway and PI3 K-AKT signaling pathway, and the key targets included mitogen activated protein kinases 3(MAPK3), proto-oncogene tyrosine-protein kinase Src(SRC), mitogen activated protein kinases 1(MAPK1), epidermal growth factor receptor(EGFR), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform(PIK3 CA), peroxisome proliferators-activated receptor gamma(PPARG), nitric oxide synthase 3(NOS3), prostaglandin G/H synthetase 2(PTGS2), matrix metalloproteinase-9(MMP9), and vascular endothelial growth factor A(VEGFA). TCM for regulating Qi mainly exerted anti-inflammatory and neuroprotective effects by acting on MAPK signaling pathway and PI3 K-AKT signaling pathway, and the key targets included mitogen activated protein kinases 8(MAPK8), SRC, mitogen activated protein kinases 14(MAPK14), and RAC-alpha serine/threonine-protein kinase(AKT1), mitogen activated protein kinases 3(MAPK3). Based on the activity spectrum of targets, the targets of the TCM for activating blood and the targets of the TCM for regulating Qi were analyzed to provide reference for the study of efficacy targets of TCM, and also provide some scientific basis for clinical application.
Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional ; Protein Interaction Maps ; Qi ; Vascular Endothelial Growth Factor A

To explore the mechanism of ovarian toxicity of Hook. F. (TwHF) by network pharmacology and molecular docking. The candidate toxic compounds and targets of TwHF were collected by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Comparative Toxicogenomics Database (CTD). Then, the potential ovarian toxic targets were obtained from CTD, and the target genes of ovarian toxicity of TwHF were analyzed using the STRING database. The protein-protein interaction (PPI) network was established by Cytoscape and analyzed by the cytoHubba plug-in to identify hub genes. Additionally, the target genes of ovarian toxicity of TwHF were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses by using the R software. Finally, Discovery Studio software was used for molecular docking verification of the core toxic compounds and the hub genes. Nine candidate toxic compounds of TwHF and 56 potential ovarian toxic targets were identified in this study. Further network analysis showed that the core ovarian toxic compounds of TwHF were triptolide, kaempferol and tripterine, and the hub ovarian toxic genes included , , , , , , , , and . Besides, the GO and KEGG analysis indicated that TwHF caused ovarian toxicity through oxidative stress, reproductive system development and function, regulation of cell cycle, response to endogenous hormones and exogenous stimuli, apoptosis regulation and aging. The docking studies suggested that 3 core ovarian toxic compounds of TwHF were able to fit in the binding pocket of the 10 hub genes. TwHF may cause ovarian toxicity by acting on 10 hub genes and 140 signaling pathways.
Drugs, Chinese Herbal/toxicity* ; Medicine, Chinese Traditional ; Molecular Docking Simulation ; Network Pharmacology ; Protein Interaction Maps