DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

ABSTRACT:

Background

Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs.

Results

In this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a het

SUBMITTER: Deng L 

PROVIDER: S-EPMC7659092 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature