Pathway-guided architectures for interpretable AI in biological research

Understanding the dysregulation of complex biological pathways is essential for uncovering molecular mechanisms and identifying novel therapeutic opportunities for complex diseases. In recent years, deep learning (DL) models have shown great potential in modeling biological multi-omics data; however, their "black box" nature limits their application in biological and clinical translation. Knowledge-guided deep learning, particularly methods based on Pathway-Guided Interpretable Deep Learning Architectures (PGI-DLA), aims to improve model performance and interpretability by integrating prior pathway knowledge into the model structure. Here, we review the current progress in PGI-DLA, focusing on omics compatibility, architectural design, feature interpretation, and biological and clinical applications. For widely used pathway databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Reactome, and MSigDB, we summarize their differences in knowledge scope, hierarchical structure, level of detail and curation focus. We discuss how the choice of database impacts model design, performance, and interpretability. This review provides valuable guidance for selecting and optimizing pathway databases to implement PGI-DLA to translate omics data into actionable biological and clinical insights.