Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)—diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking—has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large‐scale multi‐omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.