Methods Of Gene Function Prediction Their Benefits And Drawbacks

Authors

  • Maram Y. Al-Safarini

Abstract

Using microarray studies, It is now possible to quantitatively track the levels of expression of genetic variants in different laboratory conditions on a continuous basis. In spite of this, the practical interpretation of the entire genome of genes varies, and this is with the application of gene expression semantics. The aim of the research is how to make use of the data set to predict gene functions and characteristics. The researcher devised an algorithm to try to approximate the actual function of the genes that he could perform in the future through data sets for previous research. The study proposes a new method for elucidating the genome level function of genes not falling under a specific classification called Fuzzy Nearest Clusters. This algorithm works in two stages. The first stage is an assembly stage that comes in the form of a hierarchy, and it is an initial stage to detect subsets or groups of similar or homologous genes that may have been expressed in the heterogeneous functional categories that may appear. As for the second stage, it is the classification stage, and at this stage it predicts the functions of the genes that have not been classified according to the similarities corresponding to the discovered functional groups. According to the results of the experiment as a result of the data related to the gene expression of the yeast, it was found that this algorithm has the ability to correctly predict the functions of the genes even that have multiple functions. Prediction efficiency is also more independent of the basic heterogeneity of diverse functional groups, to other approaches to predicting conventional gene function.

Published

2020-11-20

Issue

Section

Articles