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Related Experiment Video

Updated: May 31, 2026

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

eResponseNet: a package prioritizing candidate disease genes through cellular pathways.

Jialiang Huang¹, Yi Liu, Wei Zhang

¹Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Bioinformatics (Oxford, England)

|June 25, 2011

Summary

We developed eResponseNet, a tool to identify disease genes and pathways from genetic variations. This method improves upon existing approaches for understanding complex traits like type II diabetes.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) identify genetic variants linked to diseases.
Functional interpretation of these variants and their connection to complex traits remains a significant challenge.

Purpose of the Study:

To develop and validate a computational package, eResponseNet, for prioritizing candidate disease genes.
To elucidate cellular pathways associated with genetic variations contributing to complex diseases.

Main Methods:

Implementation and extension of the ResponseNet algorithm.
Prioritization of candidate disease genes through cellular pathways.
Application to a type II diabetes (T2D) case study.

Main Results:

eResponseNet demonstrates superior performance in prioritizing candidate disease genes compared to existing methods.
The package effectively reveals cellular pathways underlying genetic variations associated with diseases.
Successful application in a type II diabetes study case.

Conclusions:

eResponseNet is a valuable tool for advancing the functional interpretation of GWAS results.
The package facilitates the discovery of disease mechanisms by linking genetic variations to cellular pathways.
eResponseNet offers an improved approach for understanding the genetic basis of complex diseases.