As　a　new　epitranscriptomic　modification,　N1-methyladenosine　(m1A)　plays　an　important　role　in　the　gene　expression　regulation.　Although　some　computational　methods　were　proposed　to　predict　m1A　modification　sites,　all　of　these　methods　apply　machine　learning　predictions　based　on　the　nucleotide　sequence　features,　and　they　missed　the　layer　of　information　in　transcript　topology　and　RNA　secondary　structures.　To　enhance　the　prediction　model　of　m1A　RNA　methylation,　we　proposed　a　computational　framework,　ISGm1A,　which　stands　for　integration　sequence　features　and　genomic　features　to　improve　the　prediction　of　human　m1A　RNA　methylation　sites.　Based　on　the　random　forest　algorithm,　ISGm1A　takes　advantage　of　both　conventional　sequence　features　and　75　genomic　characteristics　to　improve　the　prediction　performance　of　m1A　sites　in　human.　The　results　of　five-fold　cross　validation　and　independent　test　show　that　ISGm1A　outperforms　other　prediction　algorithms　(AUC　=　0.903　and　0.909).　In　addition,　through　analyzing　the　importance　of　features,　we　found　that　the　genomic　features　play　a　more　important　role　in　site　prediction　than　the　sequence　features.　Furthermore,　with　ISGm1A,　we　generated　a　high　accuracy　map　of　m1A　by　predicting　all　adenines　sites　in　the　transcriptome.　The　data　and　the　results　of　the　study　are　freely　accessible　at:　https://github.com/lianliu09/m1a_prediction.git.　©　2013　IEEE.