Construction of Functional Interaction Networks through Consensus Localization Predictions of the Human Proteome

2009 ◽  
Vol 8 (7) ◽  
pp. 3367-3376 ◽  
Author(s):  
Solip Park ◽  
Jae-Seong Yang ◽  
Sung Key Jang ◽  
Sanguk Kim
Keyword(s):  
Human Proteome ◽  
Interaction Networks ◽  
Functional Interaction

scholarly journals 14-3-3 phosphoprotein interaction networks – does isoform diversity present functional interaction specification?

2012 ◽  
Vol 3 ◽  
Author(s):  
Anna-Lisa Paul ◽  
Fiona C. Denison ◽  
Eric R. Schultz ◽  
Agata K. Zupanska ◽  
Robert J. Ferl
Keyword(s):  
Interaction Networks ◽  
Functional Interaction ◽  
Isoform Diversity

scholarly journals Integrating Multiple Interaction Networks for Gene Function Inference

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 30 ◽  
Author(s):  
Jingpu Zhang ◽  
Lei Deng
Keyword(s):  
State Of The Art ◽  
Interaction Networks ◽  
Dimensional Representation ◽  
Functional Interaction ◽  
High Throughput Data ◽  
Multiple Networks ◽  
The Past ◽  
Proteomic Data ◽  
Multiple Interaction ◽  
Low Dimensional

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

scholarly journals Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Large Scale ◽  
Global Analysis ◽  
Interaction Network ◽  
Interaction Networks ◽  
Microbial Pathogens ◽  
Potential Drug ◽  
Functional Interaction ◽  
Potential Drug Targets

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

scholarly journals Uncovering Phosphorylation-Based Specificities through Functional Interaction Networks

2015 ◽  
Vol 15 (1) ◽  
pp. 236-245 ◽  
Author(s):  
Omar Wagih ◽  
Naoyuki Sugiyama ◽  
Yasushi Ishihama ◽  
Pedro Beltrao
Keyword(s):  
Interaction Networks ◽  
Functional Interaction

scholarly journals Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

2012 ◽  
Vol 6 (1) ◽  
pp. 112 ◽  
Author(s):  
Mohammed Alshalalfa ◽  
Gary D Bader ◽  
Anna Goldenberg ◽  
Quaid Morris ◽  
Reda Alhajj
Keyword(s):  
Prostate Cancer ◽  
Interaction Networks ◽  
Cancer Case ◽  
Prostate Cancer Case ◽  
Functional Interaction ◽  
High Influence

scholarly journals ConsensusPathDB—a database for integrating human functional interaction networks

2008 ◽  
Vol 37 (suppl_1) ◽  
pp. D623-D628 ◽  
Author(s):  
Atanas Kamburov ◽  
Christoph Wierling ◽  
Hans Lehrach ◽  
Ralf Herwig
Keyword(s):  
Interaction Networks ◽  
Functional Interaction

scholarly journals Cystic Fibrosis: Systems Biology Analysis from Homozygous p.Phe508del Variant Patients’ Samples Reveals Perturbations in Tissue-Specific Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Joice de Faria Poloni ◽  
Thaiane Rispoli ◽  
Maria Lucia Rossetti ◽  
Cristiano Trindade ◽  
José Eduardo Vargas
Keyword(s):  
Cystic Fibrosis ◽  
Enrichment Analysis ◽  
Autosomal Recessive Disorder ◽  
Bronchial Epithelium ◽  
Interaction Networks ◽  
Homozygous State ◽  
Functional Interaction ◽  
Tissue Specific ◽  
Ppi Networks ◽  
Cf Transmembrane Conductance Regulator

Cystic fibrosis (CF) is an autosomal recessive disorder, caused by diverse genetic variants for the CF transmembrane conductance regulator (CFTR) protein. Among these, p.Phe508del is the most prevalent variant. The effects of this variant on the physiology of each tissue remains unknown. This study is aimed at predicting cell signaling pathways present in different tissues of fibrocystic patients, homozygous for p.Phe508del. The study involved analysis of two microarray datasets, E-GEOD-15568 and E-MTAB-360 corresponding to the rectal and bronchial epithelium, respectively, obtained from the ArrayExpress repository. Particularly, differentially expressed genes (DEGs) were predicted, protein-protein interaction (PPI) networks were designed, and centrality and functional interaction networks were analyzed. The study reported that p.Phe508del-mutated CFTR-allele in homozygous state influenced the whole gene expression in each tissue differently. Interestingly, gene ontology (GO) term enrichment analysis revealed that only “neutrophil activation” was shared between both tissues; however, nonshared DEGs were grouped into the same GO term. For further verification, functional interaction networks were generated, wherein no shared nodes were reported between these tissues. These results suggested that the p.Phe508del-mutated CFTR-allele in homozygous state promoted tissue-specific pathways in fibrocystic patients. The generated data might further assist in prediction diagnosis to define biomarkers or devising therapeutic strategies.

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