AUTHOR=Chen Min , Deng Yingwei , Li Ang , Tan Yan 

TITLE=Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.798632

DOI=10.3389/fgene.2022.798632

ISSN=1664-8021

ABSTRACT=Long noncoding RNA (lncRNA), a type of noncoding RNA more than 200 nucleotides long, is related to various complex diseases. Predicting the potential lncRNA–disease association is important to understand the disease pathogenesis, develop new drugs, and 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, which can be used as a useful supplement to experimental methods. Owing to the low prediction accuracy of existing calculation methods, this study proposes an integrated method called LPARP, which is based on label-propagation algorithm and random projection to predict the lncRNA–disease association method. 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. Results show that LAPRP achieves good prediction results on three different datasets, 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. In summary, 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.