Accumulating evidence demonstrate that miRNAs can be treated as critical biomarkers
in various complex human diseases. Thus, the identifications on potential miRNA-disease associations
have become a hotpot for providing better understanding of disease pathology in this field.
Recently, with various biological datasets, increasingly computational prediction approaches have
been designed to uncover disease-related miRNAs for further experimental validation. To improve
the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease
associations to enhance the miRNA functional similarity network and disease similarities of
known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated
that machine learning methods would become an effective biological resource for clinical experimental
Keywords: microRNA, disease, lung cancer, network consistency projection, biological resources, pathology.
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