Abstract
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.
Highlights
Non-coding RNAs can be classified broadly in two types: small non-coding RNAs and long noncoding RNAs that are more than 200 nucleotides (Kapranov et al, 2007; Kung et al, 2013)
This lncRNAdisease network consisted of 304,868 weighted long-non-coding RNAs (lncRNAs)-disease edges, where each link represents a predicted association between a disease and lncRNA that is proximal to its corresponding disease genes
Respiratory diseases are a class of genetically complex diseases where the molecular and regulatory genomic underpinnings, and the role of lncRNAs, are not well understood
Summary
Non-coding RNAs can be classified broadly in two types: small non-coding RNAs and long noncoding (lnc) RNAs that are more than 200 nucleotides (Kapranov et al, 2007; Kung et al, 2013). LncRNAs are discrete transcription units located in sequence space, which do not overlap protein coding genes (Kung et al, 2013). LncRNAs have received widespread attention due to their diverse roles in biological regulation, developmental processes, and diseases (Mercer et al, 2009; Orom et al, 2010; Moran et al, 2012; Sun and Kraus, 2015; Ulitsky, 2016). Increasing evidence suggests that the regulatory role of lncRNAs in biological processes often involves interactions with proteins (Ferre et al, 2016; Xiao et al, 2017). The impact of each lncRNA may be determined by its ability to perform numerous tasks in the cell by interacting with proteins, DNA and RNA molecules
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
