Biomarker development is essential in modern health care, especially in individualization of treatments and precision medicine. Omics has attracted much attention to support discovery of novel biomarkers. However, of the estimated 1M biomarkers identified in omics studies only a small fraction (<1%) has achieved FDA approval. Failures in marker development equate to lost resources, time, and credibility for the field. I aim to use Artificial Intelligence methods to efficiently mine big biomedical data and quickly assess combinations of biomarkers from large-scale omics and clinical data. These AI-driven technologies will be used to discover highly predictive biomarkers of cardiovascular disease (CVD) and cancer diseases. By speeding up the translation of omics-driven biomarker models for clinical utility, the proposed study will bring us closer to the future of non-invasive and highly accurate medical tests, which will be accessible and affordable to everyone.