Abstract
Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.
Highlights
MicroRNA is a small non-coding, single stranded and endogenous RNA molecule found in plants, animals, and some viruses, which functions in regulation of the gene expression by targeting mRNAs for cleavage or translational repression at the post-transcriptional level (Ambros, 2001, 2004; Bartel, 2004; Meister and Tuschl, 2004)
We implemented Leave-one-out cross validation (LOOCV) and five-fold cross validation to evaluate the performance of Graph Regression for MiRNA-Disease Association prediction (GRMDA)
During LOOCV, each one of the known miRNA-disease associations will be left out in turn to be considered as test sample
Summary
MicroRNA (miRNA) is a small non-coding, single stranded and endogenous RNA molecule (containing 21∼24 nucleotides) found in plants, animals, and some viruses, which functions in regulation of the gene expression by targeting mRNAs for cleavage or translational repression at the post-transcriptional level (Ambros, 2001, 2004; Bartel, 2004; Meister and Tuschl, 2004). Increasing researches have demonstrated that the miRNAs play crucial roles at multiple stages of the biological processes (Lee et al, 1993), such as early cell growth, proliferation (Cheng et al, 2005), differentiation (Miska, 2005), development (Karp and Ambros, 2005), aging (Bartel, 2009), apoptosis (Skalsky and Cullen, 2011), and so on. The dysregulation of the miRNAs has been confirmed as a main reason of aberrant cell behavior and important human complex diseases by many studies (Griffiths-Jones et al, 2006). More and more miRNAs have been verified to have associations with the development processes of many human diseases in experiments (Lynam-Lennon et al, 2009; Meola et al, 2009). Considering there are some verified miRNA-disease datasets which can be treated as materials for prediction, we can develop computational models to rank and predict potential miRNA-disease associations
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