Enhancing Transcription Factor Prediction via Domain Knowledge Integration With Logic Tensor Networks

Transcription factors (TFs) play a pivotal role in regulating gene expression, making accurate TF prediction critical for elucidating gene regulatory mechanisms. However, existing approaches face two key limitations: (1) traditional methods struggle with low accuracy, and (2) deep learning models require large datasets yet often lack interpretability, especially when domain-specific biological knowledge is underutilized. To address these challenges, we present LTN-TFpredict, a neurosymbolic framework that integrates Logic Tensor Networks (LTNs) with deep learning to simultaneously enhance accuracy and interpretability. Our model leverages pre-trained protein language models for high-dimensional sequence embeddings and incorporates logical constraints derived from five key TF-related motifs: zinc fingers, leucine zippers, basic helix-loop-helix, forkhead, and winged helix-turn-helix, to guide learning with biologically meaningful rules. This integration enforces consistency with known TF characteristics, improving both predictive performance and biological validity. Experimental results demonstrate that LTN-TFpredict consistently outperforms traditional classifiers (TFpredict, XGBoost), CNN-based models (DeepTFactor, ProtCNN), transformer-based architectures (ESM-TFpredict, ProtT5), and two alternative strategies: multi-loss optimization and motif-based data augmentation, achieving state-of-the-art accuracy while preserving strict logical compliance with domain knowledge. By bridging deep learning and symbolic reasoning, LTN-TFpredict offers a robust, interpretable, and biologically grounded solution for TF prediction, advancing the application of neurosymbolic AI in computational biology.