GCRIS

The dysregulated gene expression brings about a variety of diseases, and dysregulation of microRNA (miRNA) has a wide impact on disease development and cellular physiology. Thus, miRNAs play important roles in a variety of fundamental and significant biological processes related to human diseases. There are a lot of research about changes in the function of miRNAs have been published in many human diseases. Computational methods serve as a complementary process to traditional wet-lab experiments, which require many resources and time in terms of detecting potential miRNA-Disease associations. Furthermore, there is a need to present a novel approach that allows assignment of an unknown miRNA to its most likely species. An easy way to filter new data would be to ensure that the new miRNA is classified below the maximum distance to the species known to originate from. In this thesis, a computational model has been proposed for identifying miRNA-disease and miRNA-Species associations by depicting the miRNAs with their k-mer sequence representation and by utilizing machine learning methodologies. The difference of our approach is which we reveal disease and species associated the sequences of miRNA store information. This put a question about the miRNA's chemical compounds and their associations with different types of species and diseases. With this study, the new disease-disease and species-Species associations disclosed can be calculated for many different species and diseases, these approaches can develop to species and disease classification. Lastly, our study may open a door to redefine species and diseases classifications which have been used nowadays, also it may provide the improvement of treatment strategies and early diagnosis

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