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Related Experiment Video

Updated: Jun 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

Minghong Fang, Xiaohua Hu, Yan Wang

IEEE Transactions on Nanobioscience

|June 17, 2015

Summary

Prioritizing disease-causing genes is crucial. A new method, NDRC, effectively identifies isolated and loosely connected genes, outperforming existing approaches for better drug design and disease mechanism understanding.

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Genetics and Genomics
Bioinformatics
Computational Biology

Background:

Accurate disease-causing gene prioritization is vital for understanding disease mechanisms and developing targeted therapies.
Existing methods often fail to prioritize isolated (dangling) or loosely connected genes within biological networks.
Current approaches prioritize highly connected genes, limiting their effectiveness for complex or poorly understood diseases.

Purpose of the Study:

To develop a novel method for prioritizing disease-causing genes that overcomes limitations of existing approaches.
To improve the identification of candidate genes, including those that are isolated or loosely connected in networks.
To enhance biomedical applications such as drug design through more accurate gene prioritization.

Main Methods:

Proposed a new disease-causing gene prioritization method named Network Diffusion and Rank Concordance (NDRC).
Evaluated NDRC using leave-one-out cross-validation across 1931 diseases with known involved genes.
Compared NDRC performance against established methods like Random Walk with Restart (RWR), VAVIEN, DADA, and PRINCE.

Main Results:

NDRC successfully ranked the true causal gene first in 849 out of 2542 evaluated cases.
NDRC significantly outperformed existing methods in identifying loosely connected disease genes.
NDRC effectively prioritized dangling genes, identifying them as potential candidate disease genes.

Conclusions:

The NDRC method offers a significant advancement in disease-causing gene prioritization, particularly for challenging gene sets.
NDRC's ability to identify isolated and loosely connected genes enhances its utility for studying complex diseases.
Application of NDRC to specific diseases revealed modular structures within complex disease-causing genes linked to distinct phenotypes.