Abstract
miRNA plays an important role in many biological processes, and increasing evidence shows that miRNAs are closely related to human diseases. Most existing miRNA-disease association prediction methods were only based on data related to miRNAs and diseases and failed to effectively use other existing biological data. However, experimentally verified miRNA-disease associations are limited, there are complex correlations between biological data. Therefore, we propose a novel Three-layer heterogeneous network Combined with unbalanced Random Walk for MiRNA-Disease Association prediction algorithm (TCRWMDA), which can effectively integrate multi-source association data. TCRWMDA based not only on the known miRNA—disease associations, also add the new priori information (lncRNA–miRNA and lncRNA–disease associations) to build a three-layer heterogeneous network, lncRNA was added as the transition path of the intermediate point to mine more effective information between networks. The AUC value obtained by the TCRWMDA algorithm on 5-fold cross validation is 0.9209, compared with other models based on the same similarity calculation method, TCRWMDA obtained better results. TCRWMDA was applied to the analysis of four types of cancer, the results proved that TCRWMDA is an effective tool to predict the potential miRNA-disease association. The source code and dataset of TCRWMDA are available at: https://github.com/ylm0505/TCRWMDA.
Highlights
MiRNAs are widely found in eukaryotes and regulate the expression of other genes. miRNA is very important for the control of animal development and physiology (Victor, 2004). miRNA is involved in regulating cell differentiation (Lee et al, 1993)and plays an important role in many biological processes, including cell cycle progression and apoptosis (Brennecke et al, 2003)
Calin et al published the first study that microRNAs linked to cancer in 2002, there was a Heterogeneous Network and Unbalanced Random Walk significant association between decreased levels of both miRNAs and chronic lymphoblastic leukemia, suggesting a potential relationship between miRNA and cancer (Calin et al, 2002). miRNA is an important factor in tumorigenesis, and the artificial regulation of some miRNAs may lead to the occurrence or apoptosis of tumors, which depends on the regulation of miRNA (Yang et al, 2009)
Based on the idea of unbalanced bi-random walk, we proposed three-layer heterogeneous network combined with unbalanced random walk for miRNA-disease association prediction algorithm
Summary
MiRNAs are widely found in eukaryotes and regulate the expression of other genes. miRNA is very important for the control of animal development and physiology (Victor, 2004). miRNA is involved in regulating cell differentiation (Lee et al, 1993)and plays an important role in many biological processes, including cell cycle progression and apoptosis (Brennecke et al, 2003). MiRNA is involved in regulating cell differentiation (Lee et al, 1993)and plays an important role in many biological processes, including cell cycle progression and apoptosis (Brennecke et al, 2003). With the development of miRNA research, the association between miRNA and disease has been extended to many types of cancer, including leukemia and lung cancer (Johnson et al, 2005; Bandyopadhyay et al, 2010), breast cancer, and colon cancer (Michael et al, 2003), and so on, exploring the relationship between miRNA and disease has become the subject of many kinds of cancer research. There is an urgent need to develop a powerful computational method to predict the potential disease-related miRNAs, possible candidate miRNAs with higher prediction score were obtained by computational methods can reduce the time and cost of biological experiment
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