LPI-ETSLP: lncRNA–protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction

摘要

Hongsheng Liu *acdrnRNA–protein interactions are essential for understanding many important cellular processes. In particular,rnlncRNA–protein interactions play important roles in post-transcriptional gene regulation, such as splicing,rntranslation, signaling and even the progression of complex diseases. However, the experimental validationrnof lncRNA–protein interactions remains time-consuming and expensive, and only a few theoreticalrnapproaches are available for predicting potential lncRNA–protein associations. Here, we presentedrneigenvalue transformation-based semi-supervised link prediction (LPI-ETSLP) to uncover the relationshiprnbetween lncRNAs and proteins. Moreover, it is semi-supervised and does not need negative samples.rnBased on 5-fold cross validation, an AUC of 0.8876 and an AUPR of 0.6438 have demonstrated itsrnreliable performance compared with three other computational models. Furthermore, the case studyrndemonstrated that many lncRNA–protein interactions predicted by our method can be successfully confirmedrnby experiments. It is indicated that LPI-ETSLP would be a useful bioinformatics resource forrnbiomedical research studies.