divan accurate identification of noncoding diseasespecific risk variants using multiomics profiles understanding the link between noncoding sequence variants identified in genomewide association studies and the pathophysiology of complex diseases remains challenging due to a lack of annotations in noncoding regions to overcome this we developed divan a novel feature selection and ensemble learning framework which identifies diseasespecific risk variants by leveraging a comprehensive collection of genomewide epigenomic profiles across cell types and factors along with other static genomic features divan accurately and robustly recognizes noncoding diseasespecific risk variants under multiple testing scenarios among all the features histone marks especially those marks associated with repressed chromatin are often more informative than others