Abstract
Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.
Highlights
Long noncoding RNAs are more than 200 nucleotides long and lacks protein-coding RNAs (Peng et al, 2019)
We proposed an Long noncoding RNA (lncRNA)–disease association prediction method called LPARP, which is based on a label-propagation algorithm and random projection
After the model is trained, the true positive rate (TPR) and false-positive rate (FPR) are calculated to draw the receiver operating characteristic (ROC) curve according to the TPR and FPR under different thresholds
Summary
Long noncoding RNAs (lncRNAs) are more than 200 nucleotides long and lacks protein-coding RNAs (Peng et al, 2019). Studies have shown that lncRNAs are closely related to biological processes such as chromatin modification, transcription, translation, splicing, and epigenetic regulation (Wang and Chang, 2011; Wapinski and Chang, 2011; Song et al, 2014; Sun et al, 2017; Tian et al, 2021; Peng et al, 2021). The abnormal function of lncRNAs can reportedly lead to abnormal cell behavior, and lncRNAs are related to the occurrence and development of many human diseases. Wang et al [5] found that lncRNA PVT1 promotes the progression of melanoma through endogenous sponge cell miR-26b, and Cai et al (2018) found that BCAR4 can activate the GLI2 signaling pathway in prostate cancer. Experimentally identifying the association between lncRNAs and diseases through biotechnology is expensive and laborious. Increased attention is being paid to predicting the association between lncRNAs and diseases by computational prediction method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
