FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model

Abstract

Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions and identifying potential lncRNA-disease associations. However, little effort has been attempt to measure the functional similarity among lncRNAs on a large scale. In this study, we developed a Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM) based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases. The performance improvement of FMLNCSIM mainly comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. To evaluate the effectiveness of FMLNCSIM, we further combined it with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA). As a result, the integrated model, LRLSLDA-FMLNCSIM, achieve good performance in the frameworks of global LOOCV (AUCs of 0.8266 and 0.9338 based on LncRNADisease and MNDR database) and 5-fold cross validation (average AUCs of 0.7979 and 0.9237 based on LncRNADisease and MNDR database), which significantly improve the performance of previous classical models. It is anticipated that FMLNCSIM could be used for searching functionally similar lncRNAs and inferring lncRNA functions in the future researches.

The Evolution and Expression Pattern of Human Overlapping lncRNA and Protein-coding Gene Pairs

“…Potential lncRNA-disease associations have been identified by computational models3839404142 and aberrant expression of antisense RNA may contribute to cancers434445. As co-expression between overlapping partners has been frequently reported4647, we investigated whether there existed any signature in dysregulated coordinated expression of lncRNA-coding pairs using an RNA sequencing dataset of 369 cancer samples9.…”

LINC00978 predicts poor prognosis in breast cancer patients

“…Nowadays, many computational models have been developed to predict disease-lncRNA associations, which have shown effective prediction ability. For example, Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM)38 and Improved LNCRNA functional SIMilarity calculation model (ILNCSIM)39 can obtain new lncRNA–disease association prediction. Gao et al .…”

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

“…In 2015, novel lncRNA functional similarity calculation models (LNCSIM) [14] were provided by Chen et al By integrating LRLSLDA and LNCSIM, the performance was enhanced. Recently, a new lncRNA functional similarity calculation model, FMLNCSIM (Fuzzy Measure-Based LncRNA Functional Similarity Calculation Model) [15], has been developed; this new model has a web interface (http://219.219.60.245/) for users' convenience. Considering that nowadays the experimentally confirmed data of miRNA-disease associations are much easier to obtain than the ones of lncRNA-disease, Chen [16] utilised the miRNA-disease association and miRNA-lncRNA interaction to identified lncRNA-disease association.…”