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

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

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

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