Enhancing transcription factor regulatory network analysis through data balancing and representation learning.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Transcription factor (TF) and target gene interactions are crucial for gene regulation and are implicated in diseases like cancer.
• Experimental methods for TF-target prediction are costly and lack scalability.
• Data imbalance in machine learning models hinders accurate prediction of TF-target interactions.

Purpose of the Study:

• To develop a novel computational framework for enhanced prediction of transcription factor-target gene interactions.
• To address the challenge of data imbalance in TF-target prediction models.
• To improve the accuracy and generalizability of TF-target interaction prediction for biomedical applications.

Main Methods:

• Integration of K-means++ clustering with a data balancing strategy to mitigate bias towards low-frequency TFs.
• Application of deep learning techniques, including random walk sampling and skip-gram embeddings, for biological network representation.
• Utilizing a five-fold cross-validation approach to evaluate model performance.

Main Results:

• The proposed method significantly enhances the accuracy of TF-target interaction prediction.
• Achieved a superior average Area Under the Curve (AUC) of 0.9452 ± 0.0047.
• Demonstrated improved model generalization by effectively addressing data imbalance.

Conclusions:

• The novel framework offers a robust solution for accurate TF-target interaction prediction, overcoming data imbalance challenges.
• This approach has significant implications for advancing molecular biology and biomedical research.
• Facilitates TF-target gene discovery and aids in the development of novel therapeutic strategies.