B4185. LPI-IBWA:Predicting lncRNA-protein Interactions Based on Improved Bi-Random Walk Algorithm

B4185. LPI-IBWA:Predicting lncRNA-protein Interactions Based on Improved

Bi-Random Walk Algorithm

Minzhu Xie1, Hao Wang1 and Ruijie Xi1

1Hunan Normal University

Abstract:

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological

processes such as post-transcriptional gene regulation, splicing, and translation by combining with

corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions

of lncRNAs.The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses

similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and

protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known

neighbors algorithm are utilized to update the values for the potential interaction entries in the initial

lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA

similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, a

Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation

experiments on a benchmark dataset show that the AUC and AUPR of LPI-IBWA are 0.920 and 0.736,

respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental

results of case studies on a novel dataset also illustrate that LPI-IBWA could efficiently predict potential

lncRNA-protein interactions.