Mutations　in　and　dysregulation　of　long　non-coding　RNAs　(lncRNAs)　are　closely　associated　with　the　development　of　various　human　complex　diseases,　but　only　a　few　lncRNAs　have　been　experimentally　confirmed　to　be　associated　with　human　diseases.　Predicting　new　potential　lncRNA-disease　associations　(LDAs)　will　help　us　to　understand　the　pathogenesis　of　human　diseases　and　to　detect　disease　markers,　as　well　as　in　disease　diagnosis,　prevention　and　treatment.　Computational　methods　can　effec-tively　narrow　down　the　screening　scope　of　biological　experi-ments,　thereby　reducing　the　duration　and　cost　of　such　experiments.　In　this　review,　we　outline　recent　advances　in　computational　methods　for　predicting　LDAs,　focusing　on　LDA　databases,　lncRNA/disease　similarity　calculations,　and　advanced　computational　models.　In　addition,　we　analyze　the　limitations　of　various　computational　models　and　discuss　future　challenges　and　directions　for　development.