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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

In silico gene prioritization by integrating multiple data sources.

Yixuan Chen¹, Wenhui Wang, Yingyao Zhou

¹Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America.

|July 7, 2011

Summary

This summary is machine-generated.

This study introduces a novel gene prioritization framework to identify disease-causing genes. The approach integrates diverse data, outperforming existing methods and aiding in genetic research and disease understanding.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Identifying disease genes is critical for understanding pathogenesis and improving diagnostics.
Existing methods for gene prioritization often rely on limited data sources.
Integrating multiple heterogeneous data sources offers a more comprehensive approach.

Purpose of the Study:

To develop an expandable gene prioritization framework integrating multiple heterogeneous data sources.
To utilize a unified graphic representation for defining gene-gene and gene-disease relationships.
To establish an adaptive threshold for selecting candidate genes for validation.

Main Methods:

A unified graphic representation to integrate multiple heterogeneous data sources.
Diffusion kernel measure to define gene-gene and gene-disease relationships based on network topology.
Normalization of relationship measures across networks for candidate gene ranking.
Adaptive threshold score for selecting candidate genes based on data source informativeness.

Main Results:

The proposed framework consistently outperformed two state-of-the-art programs in large-scale cross-validation across 110 disease families.
A case study on Parkinson disease (PD) identified four high-ranking candidate genes (UBB, SEPT5, GPR37, TH), all implicated in the PD pathway.
The gene TH, identified as a candidate, was recently observed in a deletion within a PD patient, supporting its role in pathogenesis.

Conclusions:

The developed gene prioritization framework is effective and adaptable for identifying disease-associated genes.
The approach offers a significant improvement over existing methods, enhancing genetic studies.
The integrated web tool facilitates genetic research by assisting scientists in candidate gene selection.