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Related Experiment Video

Updated: Aug 6, 2025

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

A gene pathway enrichment method based on improved TF-IDF algorithm.

Shutan Xu^1,2, Yinhui Leng¹, Guofu Feng¹

¹College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.

Biochemistry and Biophysics Reports

|March 16, 2023

Summary

This summary is machine-generated.

This study introduces GIGSEA, a novel gene pathway enrichment method using TF-IDF and gene interactions. It identifies specific phenotype-related pathways more efficiently than traditional methods.

Keywords:

Gene interaction Gene set enrichment analysis Pathway enrichment TF-IDF

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Last Updated: Aug 6, 2025

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

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Published on: October 27, 2017

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene pathway enrichment analysis is crucial for understanding biological networks.
Existing methods often overlook gene interactions, focusing solely on local gene importance.
This limitation impacts the accuracy and specificity of pathway identification.

Purpose of the Study:

To develop an improved gene pathway enrichment method (GIGSEA).
To incorporate gene interaction data for enhanced pathway analysis.
To increase the efficiency and specificity of identifying phenotype-related pathways.

Main Methods:

Utilized an improved Term Frequency-Inverse Document Frequency (TF-IDF) algorithm.
Integrated gene interaction data to assess gene influence.
Calculated gene influence based on local pathway importance and global specificity.

Main Results:

GIGSEA demonstrated higher efficiency in identifying enriched pathways.
The proposed method successfully found more specific pathways related to phenotypes.
Experimental results showed significant improvement over traditional gene set enrichment analysis.

Conclusions:

GIGSEA offers a more effective approach to gene pathway enrichment analysis.
Incorporating gene interactions and global specificity enhances pathway identification.
This method provides a valuable tool for biological research and phenotype-related studies.