Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Frontiers in Physiology
Marissa SumathipalaAmitabh Sharma

Abstract

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein-protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardio...Continue Reading

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Citations

Aug 28, 2020·Frontiers in Cell and Developmental Biology·Zihao LiuNan Du
Oct 31, 2020·Frontiers in Cell and Developmental Biology·Paola StolfiPaolo Tieri
May 11, 2021·Frontiers in Genetics·Genís Calderer, Marieke L Kuijjer

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Methods Mentioned

BETA
two-hybrid

Software Mentioned

ComiRNet
LncRNADisease
lncRInter
KATZLDA
LION
RPI
GrwLDA
HCLUS
DisGeNET
LP

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