Exploring potential transcription factors and their regulatory relationships based on asymmetric covariance natural vector encoding method and machine learning algorithms.

Abstract

Transcription factors (TFs) orchestrate cellular programs by activating or repressing gene expression in response to diverse stimuli. Although advances in experimental and computational biology have expanded our understanding of TFs, existing prediction methods still struggle to accurately capture TF-target regulatory relationships and determine their directionality (activation versus inhibition). Here, we propose ACNVE-K, an integrative framework combining k-mer decomposition with asymmetric covariance natural vector encoding to convert amino acid sequences into multidimensional feature vectors. Using Leveraging eXtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Random Forest (RF) algorithms, we constructed five predictive models for TF identification, target gene inference, and regulatory direction classification. Benchmarking analyses demonstrated that XGBoost achieved the highest predictive performance across human and mouse genomes, particularly with updated genome annotations. The 5-mer configuration provided an optimal balance between feature richness and computational efficiency. Collectively, ACNVE-K offers a robust and interpretable framework for decoding transcriptional regulation, facilitating advances in precision medicine, regulatory genomics, and machine-learning-based gene network reconstruction.