gProt: Annotating Protein Interactions Using Google and Gene Ontology

2005 ◽  
pp. 1195-1203 ◽  
Author(s):  
Rune Sætre ◽  
Amund Tveit ◽  
Martin Thorsen Ranang ◽  
Tonje S. Steigedal ◽  
Liv Thommesen ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Protein Interactions

Gene Ontology-driven inference of protein–protein interactions using inducers

2011 ◽  
Vol 28 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Stefan R. Maetschke ◽  
Martin Simonsen ◽  
Melissa J. Davis ◽  
Mark A. Ragan
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

BMC Genomics ◽  
2009 ◽  
Vol 10 (1) ◽  
pp. 288 ◽  
Author(s):  
Stefanie De Bodt ◽  
Sebastian Proost ◽  
Klaas Vandepoele ◽  
Pierre Rouzé ◽  
Yves Van de Peer
Keyword(s):  
Arabidopsis Thaliana ◽  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

2021 ◽  
Vol 12 ◽  
Author(s):  
Lun Hu ◽  
Xiaojuan Wang ◽  
Yu-An Huang ◽  
Pengwei Hu ◽  
Zhu-Hong You
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Structural Information ◽  
Computational Prediction ◽  
Biological Information ◽  
Main Role ◽  
Protein Protein Interactions ◽  
Chronic Infections ◽  
Prediction Algorithms ◽  
Ppi Networks

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

scholarly journals Tissue-specific mouse mRNA isoform networks

2019 ◽  
Author(s):  
Gaurav Kandoi ◽  
Julie A. Dickerson
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Biological Process ◽  
Regulation Of Gene Expression ◽  
Functional Networks ◽  
Sequence Information ◽  
Mrna Isoforms ◽  
Tissue Specific ◽  
Class Labels ◽  
Mrna Isoform

AbstractAlternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with “NOT” qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes.

scholarly journals Using indirect protein interactions for the prediction of Gene Ontology functions

2007 ◽  
Vol 8 (Suppl 4) ◽  
pp. S8 ◽  
Author(s):  
Hon Chua ◽  
Wing-Kin Sung ◽  
Limsoon Wong
Keyword(s):  
Gene Ontology ◽  
Protein Interactions

scholarly journals Identification of potential core genes in Kawasaki disease using bioinformatics analysis

2019 ◽  
Vol 47 (9) ◽  
pp. 4051-4058
Author(s):  
Wenbin Wu ◽  
Keyou You ◽  
Jinchan Zhong ◽  
Zhanwei Ruan ◽  
Bubu Wang
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Kawasaki Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Humoral Response ◽  
Hub Genes ◽  
Potential Core

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

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