TAGRA 2025

By transforming complicated, high-dimensional biomedical data into clinically actionable insights across the care continuum, the convergence of bioinformatics and artificial intelligence is transforming healthcare. In fields including oncology, neurology, and autoimmune illnesses, AI-driven algorithms now incorporate multi-omics profiles, medical imaging, and electronic health records to enable earlier diagnosis, molecular disease stratification, and customized treatment selection. Simultaneously, deep learning and machine learning-enhanced bioinformatics pipelines speed up in silico drug screening, biomarker validation, and target discovery, reducing development times and increasing the success rate of translational research. These developments enable intelligent clinical decision support systems that enhance rather than replace doctors by offering risk prediction, therapeutic optimization, and workflow automation. This improves patient outcomes and system efficiency. However, in order to minimize algorithmic bias and guarantee that AI-enabled bioinformatics serves a variety of patient populations, realizing this transformational potential necessitates systematic attention to data quality, model interpretability, regulatory and ethical frameworks, and equitable deployment.