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Related Experiment Video

Updated: Jun 16, 2026

Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway

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Gene prioritization in Type 2 Diabetes using domain interactions and network analysis.

Amitabh Sharma¹, Sreenivas Chavali, Rubina Tabassum

¹Functional Genomics Unit, Institute of Genomics and Integrative Biology, CSIR, Delhi, India.

|February 4, 2010

Summary

This summary is machine-generated.

Identifying Type 2 Diabetes (T2D) genes is challenging. This study introduces a novel method to prioritize T2D candidate genes using protein interaction networks and weighted values, improving disease gene discovery.

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Last Updated: Jun 16, 2026

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Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway

Published on: May 10, 2024

Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Traditional methods for identifying Type 2 Diabetes (T2D) genes have limited success.
Type 2 Diabetes may arise from disrupted protein-protein interactions affecting modular domain interactions.

Purpose of the Study:

To develop and validate a novel approach for ranking candidate genes in T2D-linked genomic regions.
To identify plausible T2D disease genes more efficiently.

Main Methods:

Integration of the Weight value (Wv) method with clustering coefficients from domain interaction networks.
Calculation of Wv based on functional relationships and domain interactions of proteins.
Prioritization of High Weight Elements (HWEs) using clustering coefficients (Ci) to identify plausible disease candidates (HWEc).

Main Results:

The Wv method demonstrated high sensitivity (0.74) and specificity (0.96) with 9-fold enrichment.
Prioritization using clustering coefficients achieved 0.32 sensitivity and 0.98 specificity, with 6.8-fold enrichment.
Out of 4052 positional candidates, 435 were ranked as most likely T2D candidates, with enrichment in metabolic and signaling pathways.

Conclusions:

Positional candidate prioritization is crucial for efficient and cost-effective disease gene discovery.
This study presents a novel computational approach for prioritizing T2D candidate genes from large genomic regions.