Abstract

Biological databases are fundamental to modern life sciences, serving as repositories for genomic sequences, protein structures, metabolic pathways, and more. With the increasing complexity and volume of biological data, artificial intelligence (AI) has become indispensable in enhancing data integration, analysis, and interpretation. This review explores the role of AI in transforming biological databases, highlighting key applications such as protein structure prediction, functional annotation, pandemic surveillance, and antibiotic resistance analysis. AI-driven tools like Alpha Fold have revolutionized protein structure determination, while UniProt leverages machine learning for automated protein function annotation. In genomic research, AI enhances text mining in PubMed through PubTator, aiding in extracting relevant biological information. AI also facilitates pathway predictions in KEGG, improves virus tracking in GISAID, and strengthens protein-protein interaction predictions in STRING. Furthermore, AI has advanced CRISPR-Cas9 off-target effect predictions, ensuring safer genome editing, and has been pivotal in analyzing antibiotic resistance through the CARD database. Despite challenges such as data heterogeneity and model interpretability, AI continues to expand the capabilities of biological databases, accelerating research, innovation, and applications in medicine, drug discovery, and personalized healthcare

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