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

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

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

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 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

OBJECTIVE: To identify the key genes and explore mechanisms in the development of myelodysplastic syndrome (MDS) by bioinformatics analysis. METHODS: Two cohorts profile datasets of MDS were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed gene (DEG) was screened by GEO2R, functional annotation of DEG was gained from GO database, gene ontology (GO) enrichment analysis was performed via Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and key genes were screened by Matthews correlation coefficient (MCC) based on STRING database. RESULTS: There were 112 DEGs identified, including 85 up-regulated genes and 27 down-regulated genes. GO enrichment analysis showed that biological processes were mainly enriched in immune response, etc, cellular component in cell membrane, etc, and molecular function in protein binding, etc. KEGG signaling pathway analysis showed that main gene enrichment pathways were primary immunodeficiency, hematopoietic cell lineage, B cell receptor signaling pathway, Hippo signaling pathway, and asthma. Three significant modules were screened by Cytoscape software MCODE plug-in, while 10 key node genes (CD19, CD79A, CD79B, EBF1, VPREB1, IRF4, BLNK, RAG1, POU2AF1, IRF8) in protein-protein interaction (PPI) network were screened based on STRING database. CONCLUSION These screened key genes and signaling pathways are helpful to better understand molecular mechanism of MDS, and provide theoretical basis for clinical targeted therapy.
Computational Biology ; Gene Expression ; Gene Expression Profiling ; Humans ; Microarray Analysis ; Myelodysplastic Syndromes/genetics* ; Protein Interaction Maps