Prediction of circRNA-disease associations based on inductive matrix completion.

ABSTRACT:

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 associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under RO

SUBMITTER: Li M 

PROVIDER: S-EPMC7118830 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature