scholarly journals Corrigendum to “Predicting lncRNA–miRNA interactions based on interactome network and graphlet interaction” [Genomics 113 (2021) 874–880]

Genomics ◽  
2021 ◽  
Author(s):  
Li Zhang ◽  
Ting Liu ◽  
Haoyu Chen ◽  
Qi Zhao ◽  
Hongsheng Liu
Keyword(s):  
Interactome Network

scholarly journals Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paola Paci ◽  
Giulia Fiscon ◽  
Federica Conte ◽  
Rui-Sheng Wang ◽  
Lorenzo Farina ◽  
...  
Keyword(s):  
Network Analysis ◽  
Interaction Network ◽  
Integrated Approach ◽  
State Transitions ◽  
Disease Genes ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Interactome Network ◽  
Module Discovery ◽  
Specific Disorders

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2013 ◽  
pp. 222-238
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Graph Theoretic ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

scholarly journals O11.4. INTERACTOME OVERLAP BETWEEN SCHIZOPHRENIA AND COGNITION

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S28-S28
Author(s):  
Elise Koch ◽  
Brin Rosenthale ◽  
Anders Lundquist ◽  
Chi-Hua Chen ◽  
Karolina Kauppi
Keyword(s):  
Cognitive Performance ◽  
Genetic Architecture ◽  
Cognitive Impairments ◽  
Long Term Potentiation ◽  
Pathway Enrichment Analysis ◽  
Healthy Individuals ◽  
Risk Genes ◽  
Interactome Network ◽  
Genetic Overlap ◽  
Network Space

Abstract Background Cognitive impairments constitute a core feature of schizophrenia, and a genetic overlap between schizophrenia and cognitive functioning in healthy individuals has been identified. However, due to the high polygenicity and complex genetic architecture of both traits, overlapping biological pathways have not yet been identified between schizophrenia and normal cognitive ability. Network medicine offers a framework to study biologically meaningful gene networks through protein-protein interactions among risk genes. Here, established network-based methods were used to further reveal the biological relatedness of schizophrenia and cognition. Methods The protein interactome was used to examine the genetic link between schizophrenia risk genes and genes associated with cognitive performance in healthy individuals. First, we used a method called network separation to examine if there is an overlap between schizophrenia and cognition in the interactome network space. Then, we used network propagation analyses to identify schizophrenia risk genes that are close to cognition-associated genes in the interactome network space. Gene ontology and pathway enrichment analysis was performed to describe the function of this gene set. Results Network separation analyses showed a profound interactome overlap between schizophrenia risk genes and genes associated with cognitive performance (SAB = -0.22, z-score = -6.80, p = 5.38e-12). We identified 140 schizophrenia risk genes that are close to cognition-associated genes in the interactome. Risk genes close to cognition were enriched for pathways including long-term potentiation and Alzheimer’s disease, and included genes with a role in neurotransmitter systems implemented in cognition, such as glutamate and dopamine, that were not part of the direct genetic overlap. Moreover, schizophrenia risk genes close to cognition included 45 druggable genes not yet used as drug targets. Discussion These results pinpoint schizophrenia risk genes of particular interest for further examination in schizophrenia patient groups to reveal the genetic architecture of cognitive impairments in schizophrenia, of which some are druggable genes with potential as candidate targets for cognitive enhancing drugs.

scholarly journals Identification of key regulators in prostate cancer from gene expression datasets of patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Irengbam Rocky Mangangcha ◽  
Md. Zubbair Malik ◽  
Ömer Küçük ◽  
Shakir Ali ◽  
R. K. Brojen Singh
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Regulatory Pathways ◽  
Scale Free ◽  
Topological Parameters ◽  
Rich Club ◽  
Self Organized ◽  
Mutational Status ◽  
Interactome Network ◽  
The Rich

Abstract Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

Cri-du-Chat Syndrome interactome network: Correlating genotypic variations to associated phenotypes

Gene Reports ◽  
2018 ◽  
Vol 11 ◽  
pp. 179-187 ◽  
Author(s):  
Giuseppe Gianini Figueiredo Leite ◽  
Hátylas Azevedo ◽  
Talita Mendes de Oliveira ◽  
Danielle Zildeana Sousa Furtado ◽  
Nilson Antonio Assunção
Keyword(s):  
Cri Du Chat Syndrome ◽  
Interactome Network
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