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Related Experiment Video

Updated: Feb 5, 2026

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression
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Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine.

Giulia Fiscon1,2, Federica Conte3,4, Lorenzo Farina5

  • 1Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, via dei Taurini 19, 00185 Rome, Italy. giulia.fiscon@iasi.cnr.it.

Genes
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

Network medicine uses protein-protein interaction (PPI) networks to find disease genes. New models explore RNA interactions and identify key genes in cancer, comparing network analysis tools like DIAMOnD and SWIM.

Keywords:
PPI networkbioinformaticsceRNAgene co-expression networknetwork medicineregulatory network

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Area of Science:

  • Network medicine
  • Computational biology
  • Systems biology

Background:

  • Protein-protein interaction (PPI) networks are crucial for understanding molecular mechanisms.
  • Non-coding RNAs, including long non-coding RNAs (lncRNAs), are key regulators in cellular processes.
  • Competing endogenous RNA (ceRNA) networks offer insights into post-transcriptional regulation.

Purpose of the Study:

  • To explore lncRNA-associated ceRNA activity in breast invasive carcinoma using a data-driven model.
  • To describe the Switch Miner (SWIM) tool for identifying switch genes in co-expression networks.
  • To compare the disease gene predictions of DIAMOnD and SWIM in cancer research.

Main Methods:

  • Analysis of human PPI network topology using the DIAMOnD algorithm to identify disease-associated genes.
  • Development of a data-driven model to investigate lncRNA-ceRNA interactions.
  • Application of the SWIM tool to gene expression data to detect switch genes critical for cell phenotype changes.

Main Results:

  • The DIAMOnD algorithm identifies disease genes by analyzing connectivity significance in PPI networks.
  • The described ceRNA model provides insights into regulatory mechanisms in breast cancer.
  • SWIM identifies critical switch genes associated with drastic cell phenotype changes, with potential applications in cancer research.

Conclusions:

  • Network medicine approaches, including PPI and ceRNA network analysis, are valuable for uncovering disease mechanisms.
  • Tools like DIAMOnD and SWIM offer complementary strategies for identifying disease-associated genes and critical regulators in cancer.
  • Further research integrating these network-based methods can advance our understanding of complex diseases.