The structure balance of gene-gene networks beyond pairwise interactions

Despite its high and direct impact on nearly all biological processes, the underlying structure of gene-gene interaction networks is investigated so far according to pair connections. To address this, we explore the gene interaction networks of the yeast Saccharomyces cerevisiae beyond pairwise interaction using the structural balance theory (SBT). Specifically, we ask whether essential and nonessential gene interaction networks are structurally balanced. We study triadic interactions in the weighted signed undirected gene networks and observe that balanced and unbalanced triads are over and underrepresented in both networks, thus beautifully in line with the strong notion of balance. Moreover, we note that the energy distribution of triads is significantly different in both essential and nonessential networks compared with the shuffled networks. Yet, this difference is greater in the essential network regarding the frequency as well as the energy of triads. Additionally, results demonstrate that triads in the essential gene network are more interconnected through sharing common links, while in the nonessential network they tend to be isolated. Last but not least, we investigate the contribution of all-length signed walks and its impact on the degree of balance. Our findings reveal that interestingly when considering longer cycles the nonessential gene network is more balanced compared to the essential network.

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Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection

Journal of Experimental Botany ◽

10.1093/jxb/erz332 ◽

2019 ◽

Vol 70 (20) ◽

pp. 5603-5616 ◽

Cited By ~ 7

Author(s):

Tianhua He ◽

Camilla Beate Hill ◽

Tefera Tolera Angessa ◽

Xiao-Qi Zhang ◽

Kefei Chen ◽

...

Keyword(s):

Flowering Time ◽

Gene Networks ◽

Statistical Power ◽

Gene Interaction ◽

High Accuracy ◽

Association Test ◽

Interaction Networks ◽

Gene Set ◽

Epistasis Analysis ◽

Gene Interaction Networks

Using gene-set association test and epistasis analysis, this research achieved higher statistical power with potentially high accuracy, and detected significant genes and gene networks that influence flowering time in barley.

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A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks

Scientific Reports ◽

10.1038/s41598-021-02671-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jorge Francisco Cutigi ◽

Adriane Feijo Evangelista ◽

Rui Manuel Reis ◽

Adenilso Simao

Keyword(s):

Gene Network ◽

Large Scale ◽

Gene Interaction ◽

Computational Method ◽

Interaction Networks ◽

Cancer Genes ◽

Gene Interaction Networks ◽

Mutation Score ◽

Number Of Genes ◽

Genomic Studies

AbstractIdentifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.

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Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

10.1101/2021.12.08.471133 ◽

2021 ◽

Author(s):

Elisabetta Sciacca ◽

Anna E.A. Surace ◽

Salvatore Alaimo ◽

Alfredo Pulvirenti ◽

Felice Rivellese ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Rna Sequencing ◽

Gene Networks ◽

Prediction Models ◽

Gene Interaction ◽

Interaction Networks ◽

Gene Interactions ◽

Specific Gene ◽

Rna Seq ◽

Gene Interaction Networks

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

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