IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations

Wang, Qi and Yan, Guiying (2019) IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations. Frontiers in Genetics, 10. ISSN 1664-8021

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Abstract

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

Item Type: Article
Subjects: STM Digital Press > Medical Science
Depositing User: Unnamed user with email support@stmdigipress.com
Date Deposited: 11 Feb 2023 08:24
Last Modified: 17 Jun 2024 06:59
URI: http://publications.articalerewriter.com/id/eprint/199

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