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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Dec 6, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Prioritizing disease biomarkers using functional module based network analysis: A multilayer consensus driven scheme.

Monica Jha¹, Swarup Roy², Jugal K Kalita³

¹Department of Computer Science & Engineering, Sikkim Manipal Institute of Technology, Majitar, India; Department of Information Technology, North Eastern Hill University, Shillong, India.

Computers in Biology and Medicine

|October 13, 2020

Summary

This summary is machine-generated.

Identifying key genes for complex diseases like Alzheimer's and breast cancer is crucial for drug discovery. This study proposes a novel network analysis approach to pinpoint these vital disease biomarker genes.

Keywords:

Biclustering Clustering Co-expression Disease pathways Gene ontology Network module RNA sequence

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Complex diseases often arise from disruptions in gene interaction networks.
Identifying critical genes (biomarkers) is essential for understanding disease mechanisms and prioritizing drug targets.

Purpose of the Study:

To develop and validate a multi-layer, consensus-driven computational scheme for identifying potential disease biomarker genes.
To pinpoint pivotal genes involved in Alzheimer's Disease and Breast Cancer.

Main Methods:

Reconstruction of functional module-guided disease sub-networks.
Application of multi-step consensus from network inference and ontological term sharing.
Centrality analysis to identify hub genes within disease-specific sub-networks.

Main Results:

The proposed scheme successfully identified key genes for Alzheimer's Disease (MAPK1, APP, CASP7) and Breast Cancer (BRCA1, BRCA2, PTEN).
Prioritized genes were validated against existing scientific evidence, confirming their significance.

Conclusions:

The multi-layer consensus approach is effective for identifying pivotal biomarker genes in complex diseases.
This method aids in prioritizing genes for further research and potential therapeutic development.