ES-MDA: Enhanced Similarity-based MiRNA-Disease Association.
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 guidance.
['Xu|Li|L|', 'Jiang|Ge-Ning|GN|']
D000465:Algorithms D014408:Biomarkers, Tumor D019295:Computational Biology D030541:Databases, Genetic D066264:Datasets as Topic D056726:Genetic Association Studies D006801:Humans D000069550:Machine Learning D035683:MicroRNAs D009369:Neoplasms
disease pathology biological resources lung cancer microRNA network consistency projection