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Predicting TF-Target Gene Association Using a Heterogeneous Network and Enhanced Negative Sampling.

Thanh Tuoi Le^1,2, Xuan Tho Dang³

¹Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam.

Bioinformatics and Biology Insights

|February 27, 2025

Summary

This study introduces a novel method for selecting enhanced negative samples to improve the prediction of transcription factor (TF)-target gene interactions, crucial for understanding biological processes and diseases.

Keywords:

Heterogeneous network TF–target gene association enhanced negative sample meta-path

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Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Identifying transcription factor (TF)-target gene interactions is vital for understanding biological mechanisms and diseases.
Experimental methods for TF-target gene identification are often laborious, expensive, and limited in scope.
Existing computational approaches primarily predict TF binding sites, not direct interactions, and struggle with robust sample dataset construction.

Purpose of the Study:

To propose a new method for selecting enhanced negative samples to improve the accuracy of predicting TF-target gene interactions.
To address the challenge of incomplete coverage in potential TF-target gene relationships due to inadequate negative sample selection in current methods.

Main Methods:

Development of a novel strategy for selecting enhanced negative samples in TF-target gene interaction datasets.
Validation of the proposed method using 5-fold cross-validation.

Main Results:

The proposed method achieved a high average area under the curve (AUC) of 0.9024 ± 0.0008.
Demonstrated significant improvement in predicting TF-target gene interactions compared to existing approaches.

Conclusions:

The developed method effectively enhances the prediction of TF-target gene interactions by improving negative sample selection.
The model exhibits high efficiency and accuracy, showing potential for broad application in large-scale biomedical research and data analysis.