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.
- lncRNA-protein Interactions Predicting Algorithm Bi-Randomlncrna-protein interactions predicting bi-random lncrna-protein interactions predicting algorithm interactions drug-target predicting bi-random lncrna-protein predicting interactions infrastructure multivariate performance predicting combinations forest-based synergistic predicting predicting indicators algorithms highlands