Abstract
A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model.
Highlights
MiRNAs are a kind of single-stranded, non-coding RNA with a length of about 20–25 nucleotides. miRNAs combine with 30untranslated regions and inhibit the translation of target mRNAs, showing a significant influence on the expression of genes after transcription [1,2,3]. miRNAs are involved in the physiological and pathological processes of mammals [4]; the development, differentiation, growth, and metabolism of cells are closely related to miRNAs [5]
leave-one-out cross validation (LOOCV) was adopted to evaluate the performance of network projection-based dual random walk with restart (NPRWR)
A novel miRNA-disease association prediction model used as training samples for model training
Summary
MiRNAs are a kind of single-stranded, non-coding RNA with a length of about 20–25 nucleotides. miRNAs combine with 30untranslated regions and inhibit the translation of target mRNAs, showing a significant influence on the expression of genes after transcription [1,2,3]. miRNAs are involved in the physiological and pathological processes of mammals [4]; the development, differentiation, growth, and metabolism of cells are closely related to miRNAs [5]. MiRNAs are a kind of single-stranded, non-coding RNA with a length of about 20–25 nucleotides. MiRNAs combine with 30untranslated regions and inhibit the translation of target mRNAs, showing a significant influence on the expression of genes after transcription [1,2,3]. MiRNAs are involved in the physiological and pathological processes of mammals [4]; the development, differentiation, growth, and metabolism of cells are closely related to miRNAs [5]. Studies have shown that miRNAs play an important role in the pathogenesis of human diseases. The transfection of miRNA-101 can affect the induction and expression of ubiquitin ligase HECTH9 in acute myeloid leukemia cells [6]; miRNA-21, an exosome derived.
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