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Updated: Jun 17, 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

Gene prioritization and clustering by multi-view text mining.

Shi Yu¹, Leon-Charles Tranchevent, Bart De Moor

¹Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B3001, Belgium. shi.yu@esat.kuleuven.be

BMC Bioinformatics

|January 16, 2010

Summary

This summary is machine-generated.

Integrating multiple text mining models improves disease gene identification. A novel multi-view approach enhances accuracy in gene prioritization and clustering, outperforming individual models for better biomedical knowledge discovery.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Text mining aids biologists in understanding disease genetics and identifying candidate genes.
Existing text mining models show variable effectiveness in disease-gene identification.
Combining multiple text mining models is challenging but potentially beneficial for refined knowledge.

Purpose of the Study:

To present a multi-view approach for biomedical knowledge retrieval using diverse controlled vocabularies.
To investigate the integration of multiple text mining views for disease gene identification tasks.
To evaluate the performance of the multi-view approach against existing methods.

Main Methods:

Utilized nine bio-ontologies to select controlled vocabularies for indexing MEDLINE gene-based text.
Treated text mining results from each vocabulary as a separate 'view'.
Integrated multiple views using multi-source learning algorithms for gene prioritization and clustering.

Main Results:

The multi-view approach demonstrated significantly superior performance in both gene prioritization and gene clustering tasks.
Systematic evaluation on real benchmark datasets confirmed the enhanced accuracy of the proposed method.
The integrated model outperformed individual text mining models and other comparative methods.

Conclusions:

Multi-view text mining is a superior and promising strategy for text-based disease gene identification.
This approach is particularly valuable when the relevance of specific vocabularies is unknown in practical research.
The findings support the integration of diverse data sources for robust biomedical knowledge discovery.