ncRNA-disease association prediction based on sequence information and tripartite network

Abstract

BackgroundCurrent technology has demonstrated that mutation and deregulation of non-coding RNAs (ncRNAs) are associated with diverse human diseases and important biological processes. Therefore, developing a novel computational method for predicting potential ncRNA-disease associations could benefit pathologists in understanding the correlation between ncRNAs and disease diagnosis, treatment, and prevention. However, only a few studies have investigated these associations in pathogenesis.ResultsThis study utilizes a disease-target-ncRNA tripartite network, and computes prediction scores between each disease-ncRNA pair by integrating biological information derived from pairwise similarity based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. In addition, we achieved an average AUC that varies from 0.75 without link cut to 0.57 with link cut methods, which outperforms a previous method using the same evaluation methodology. Furthermore, the algorithm predicted 23 ncRNA-disease associations supported by other independent biological experimental studies.ConclusionsTaken together, these results demonstrate the capability and accuracy of predicting further biological significant associations between ncRNAs and diseases and highlight the importance of adding biological sequence information to enhance predictions.

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

“…60,61 The other two methods, TPGLDA and ncPred, allocated resources from the disease to lncRNAs and other nodes, respectively, but the difference is that the resources are returned to the initial nodes. 62,63 Recommendation Algorithm-Based Methods…”

Detecting Clustered Independent Rare Variant Associations Using Genetic Algorithms

“…Alternative strategies such as using an ensemble of these methods have been proposed toward solving this problem [8] [22]. Alternative strategies have relied on unsupervised learning for identifying genes associated with complex diseases [4] [12] [10] [13] [15]. However, detecting associations under low signal-to-noise ratio scenarios remains a challenge for rare-variant association methods.…”

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges

“…Among all of the ncRNAs, long noncoding RNAs (lncRNAs) – transcripts of length above 200nt -- have aroused intense interests due to their significant roles in many biological processes and diseases, such as epigenetic modification, gene and protein expression regulation, and cancer progression [15] , [44] , [113] , [118] , [121] , [140] . Different tools have been developed to identify lncRNAs, predict their function and correlate with various diseases [10] , [100] , [2] . Many lncRNAs share similar features with classical mRNAs, such as transcription by polymerase II with a 5′-cap and 3′-polyadenylated tail, splicing pattern, sequence length, frequent accumulation in the cytoplasm, and even overlap with coding genes [135] , [136] , [96] , [117] , [151] .…”