PREDICTION OF THE UNKNOWN ASSOCIATIONS BETWEEN MICRORNA AND HUMAN DISEASES
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Abstract
MicroRNAs, also reffered as miRNAs, are non - coding RNAs (Ribo Nucleic Acid) that play a key role in regulating the expression of genes. Gene knockdown, a quick and easy way to stop expression of genes in a variety of organisms. MiRNAs play a role in both the post - transcriptional expressive regulation of genes and the RNA-level control of specific genes. Some investigations have confirmed the crucial part that microRNA (miRNA) plays in human disorders. Numerous developmental processes, comprising metabolic activities, proliferation and differentiation, cytotoxicity, neurodevelopment timing, and dopaminergic neurons destiny, have been shown to be significantly influenced by microRNAs. Changes that lead cells to decide to become malignant have been demonstrated to be caused by changing patterns of miRNAs in cells. Deficits or amplifications of microRNAs have been linked to a variety of other clinically significant disorders, including autoimmune disorder and coronary artery disease. Since, they play an important role in human diseases; they are very useful for the researchers in the identification of association among the miRNA’s and their respective human disorders. The expectation is that miRNAs will soon have a tremendous potential for the detection and treatment of many diseases thanks to great breakthroughs and quick growth in the field over the past few years. Identifying the microRNA - disease associations based on biological experiments is a tedious process which consumes time and money. Hence, various highly - efficient algorithms are used in the identification of unknown connections between the miRNA and the human diseases. A meta - research based on twenty journal papers is gathered to explore the prediction of miRNA - illness correlations. In this paper, the methodology to predict the associations of various algorithms are discussed and their performance metrics such as Area under Precision and Recall (AUPR), Area under the Curve (AUC) and other parameters are compared. The association papers were examined from a variety of angles, such as technology, method, outcome, and individual differences. The result from this study shows that these algorithms play a crucial part in identifying the unknown miRNA and human disorder associations.
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