Motivation Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play
pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are
implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is
beneficial to disease diagnosis as well as treatment. Although many computational methods
have been developed, precisely identifying lncRNA–disease associations, especially for
novel lncRNAs, remains challenging. Results In this study, we propose a method (named …

Background Currently, numerous studies indicate that circular RNA (circRNA) is associated
with various human complex diseases. While identifying disease-related circRNAs in vivo is
time-and labor-consuming, a feasible and effective computational method to predict circRNA-
disease associations is worthy of more studies. Results Here, we present a new method
called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations
prediction) to predict circRNA-disease associations. Based on known circRNA-disease …