See how our improved method for detecting coronary heart disease could have a substantial clinical impact.
Our manuscript describing the integrated genetic-epigenetic prediction of coronary heart disease (CHD) has just been published online. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic, and phenotype data to build and test a Random Forest classification model for symptomatic CHD.
You can read the published paper here.