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

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

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

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 explore the main target and signal pathway of Simiao Yongan Decoction in the treatment of psoriatic arthritis(PsA) by network pharmacology, so as to reveal the intervention mechanism of Simiao Yongan Decoction in the treatment of psoriatic arthritis. The platform of pharmacology technology of traditional Chinese medicine system(TCMSP) was used to predict and screen the active ingredients of Simiao Yongan Decoction, and GeneCards database was searched to obtain the disease target related to the psoriatic arthritis. Protein interaction network model was constructed with STRING platform; drug-component-target-disease network map was constructed with Cytoscape Software; Wayne Diagram of common target of Simiao Yongan Decoction and psoriasis arthritis was drawn with the help of ClusterProfiler R Software. At the same time, the genetic ontology(GO) enrichment analysis and the Kyoto encyclopedia of genes and genomes(KEGG) pathway analysis were conducted. Through database analysis, 1 128 targets related to 70 main active components of Simiao Yongan Decoction and psoriatic arthritis were selected. On this basis, the interaction network between Simiao Yongan Decoction and psoriatic arthritis was constructed, and 38 common targets were screened out. By GO and KEGG enrichment analysis, 135 signal pathways related to the main components of Simiao Yongan Decoction were selected. It was found that Simiao Yong-an Decoction may play a role in the treatment of psoriatic arthritis through antiviral effect, anti-inflammatory repair, protection of vascular endothelial cells, regulation of immunity and other multiple targets. The mechanism of Simiao Yongan Decoction in the treatment of psoriatic arthritis from multi-component, multi-target and multi-pathway was revealed, which provided a research direction for screening its subsequent clinical effect evaluation indexes.
Arthritis, Psoriatic ; Drugs, Chinese Herbal ; Endothelial Cells ; Humans ; Medicine, Chinese Traditional ; Protein Interaction Maps

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

OBJECTIVES: To identify the differentially expressed genes (DEGs) during the pathogenesis of periodontitis by bioinformatics analysis. METHODS: GEO2R was used to screen DEGs in GSE10334 and GSE16134. Then, the overlapped DEGs were used for further analysis. g:Profiler was used to perform Gene Ontology analysis and pathway analysis for upregulated and downregulated DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network, which was further visua-lized and analyzed by Cytoscape software. Hub genes and key modules were identified by cytoHubba and MCODE plug-ins, respectively. Finally, transcription factors were predicted via iRegulon plug-in. RESULTS: A total of 196 DEGs were identified, including 139 upregulated and 57 downregulated DEGs. Functional enrichment analysis showed that the upregulated DEGs were mainly enriched in immune-related pathways including immune system, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and chemokine receptors bind chemokines. On the contrary, the downregulated DEGs were mainly related to the formation of the cornified envelope and keratinization. The identified hub genes in the PPI network were CXCL8, CXCL1, CXCR4, SEL, CD19, and IKZF1. The top three modules were involved in chemokine response, B cell receptor signaling pathway, and interleukin response, respectively. iRegulon analysis revealed that IRF4 scored the highest. CONCLUSIONS The pathogenesis of periodontitis was closely associated with the expression levels of the identified hub genes including CXCL8, CXCL1, CXCR4, SELL, CD19, and IKZF1. IRF4, the predicted transcription factor, might serve as a dominant upstream regulator.
Computational Biology ; Gene Expression Profiling ; Humans ; Microarray Analysis ; Periodontitis ; Protein Interaction Maps