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

Abstract

Identifying interactions between transcription factors (TFs) and target genes is crucial for understanding the molecular mechanisms involved in biological processes and diseases. Traditional biological experiments used to determine these interactions are often time-consuming, costly, and limited in scale. Current computational methods mainly predict binding sites rather than direct interactions. Although recent studies have achieved high performance in predicting TF-target gene associations, they still face a significant challenge related to constructing a robust dataset of positive and negative samples. Currently, methods do not adequately focus on selecting negative samples, resulting in incomplete coverage of potential TF-target gene relationships. This article proposes a method to select enhanced negative samples to improve the prediction performance of TF-target gene interactions. Experimental results show that the proposed method achieves an average area under the curve (AUC) value of 0.9024 ± 0.0008 through 5-fold cross-validation. These results demonstrate the model's high efficiency and accuracy, confirming its potential application in predicting TF-target gene interactions across various datasets and paving the way for large-scale biomedical research.