Data Analysis for microRNA and Related Diagnoses

MicroRNAs are non-coding molecules that play a significant role in the development of the disease. MicroRNAs can act as biomarkers or independently lead to the development of a disease. Due to the large numbers of microRNAs, most of the current works focus on the creation of a new way of microRNA clustering or grouping. Today, there are a huge number of different databases that distribute open microRNAs into groups. The problem is that there is no way to evaluate such databases and created clusters. In this work, we propose a new method for assessing the distribution of microRNAs in a cluster, which in the future can be used to predict new sequential ones capable of causing disease. The proposed method can also be used for a better understanding of the mechanisms of various diseases. Since cardiovascular diseases rank first in terms of the number of deaths, they were chosen as the analyzed ones. The Human microRNA Disease Database was used as an analyzed database in this work. The obtained results show that the proposed method can analyze the created databases and can be used in further practice. The proposed model makes it possible to predict new microRNAs for given diagnoses.