A Novel Network-Based Computational Model for Prediction of Potential LncRNA⁻Disease Association.

ABSTRACT: Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA⁻disease associations, in which two novel lncRNA⁻disease weighted networks were constructed. They were first based on known lncRNA⁻disease associations and topological similarity of the lncRNA⁻disease association network, and then an lncRNA⁻lncRNA weighted

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6480945 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature