N6-methyladenosine　(m6A)　is　the　most　abundant　RNA　modification　on　mRNA　and　lncRNA　in　human.　Recent　studies　have　shown　that　it　is　implicated　in　various　critical　biological　processes,　such　as　translation　and　alternative　splicing,　and　involves　in　multiple　human　diseases,　including　cancer　and　obesity.　However,　only　a　small　number　of　RNA　methylation　sites　have　been　explicitly　associated　to　disease　conditions　with　experimental　approaches,　since　RNA　m6A　methylation　sites　may　potentially　play　a　pivotal　regulatory　role　in　a　wide　range　of　human　pathogenesis　and　should　not　be　ignored,　an　efficient　predictor　for　disease-associated　m6A　RNA　methylation　sites　becomes　a　major　challenge.　In　order　to　obtain　a　comprehensive　understanding　of　disease-associated　m6A　RNA　methylation　site,　we　purpose　here　a　computational　framework　to　integrate　the　three　different　layers　of　a　network　structure,　including　the　expression　profiles　of　genes,　the　methylation　profiles　of　m6A　RNA　methylation　sites　and　gene-disease　associations,　and　utilize　subsequently　the　Hypergenomic　test　to　predict　integrally　the　potential　disease-associated　m6A　sites.　We　show　with　a　rigorous　cross-validation　that　the　AUROC　of　the　proposed　approach　is　0.73;　and　a　number　of　predictions　are　supported　by　existing　literatures,　suggesting　our　prediction　is　helpful　for　identifying　the　novel　m6A　sites　associated　to　human　disease.　Ultimately,　the　predicted　results　are　freely　available　online　at:　http://180.208.58.19/DRRMSDB/,　which　supports　the　queries　of　diseaserelated　m6A　RNA　methylation　sites.　We　presented　a　very　first　attempt　for　computational　prediction　of　diseaseassociated　RNA　methylation　sites,　helping　researchers　of　the　field　to　understand　the　roles　of　m6A　RNA　methylation　in　human　diseases　and　facilitating　the　development　of　the　treatments.　©　VDE　VERLAG　GMBH　·　Berlin　·　Offenbach.