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CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
Published on: April 25, 2022
A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological
Shuting Jin1, Xiangxiang Zeng2, Jiansong Fang3
11Department of Computer Science, Xiamen University, Xiamen, 361005 China.
Abstract:
Disease-disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease-disease relationships by assembling four biological networks: disease-miRNA, miRNA-gene, disease-gene, and the human protein-protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease-disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease-disease relationships with miRNA-mediated pathobiological pathways.
Insights
We developed a novel network-based algorithm, mpDisNet, to identify disease relationships and comorbidities. This method accurately predicts disease connections, uncovering miRNA-mediated pathways underlying pulmonary diseases.
Area of Science:
- Computational biology
- Network medicine
- Bioinformatics
Background:
- Disease-disease relationships, or comorbidities, are vital for understanding disease progression and developing personalized medicine.
- Identifying these relationships is complex, often relying on simpler methods like gene or microRNA overlap.
Purpose of the Study:
- To develop and validate a network-based methodology for inferring disease-disease relationships.
- To identify novel disease comorbidities and their underlying miRNA-mediated pathobiological mechanisms.
Main Methods:
- Developed the meta-path-based Disease Network (mpDisNet) algorithm.
- Integrated four biological networks: disease-miRNA, miRNA-gene, disease-gene, and human protein-protein interactome.
- Employed a meta-path-based random walk and a heterogeneous skip-gram model for network representation.
Main Results:
- mpDisNet demonstrated high performance in inferring clinically reported disease-disease relationships.
- The algorithm outperformed traditional gene/miRNA-overlap approaches.
- Identified network-based comorbidities for pulmonary diseases linked to specific miRNA pathways (hsa-let-7a/b).
Conclusions:
- mpDisNet is a powerful tool for identifying disease-disease relationships using network analysis.
- The study highlights the role of miRNA-mediated pathways in disease comorbidities, particularly for pulmonary conditions.
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