A fuzzy model for the removal of uninformative gene expression profiles using statistical and entropy criteria

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Liakh I. M. № 1 (66) 39-55 Image Image

The results of research on the formation of subsets of mutually expressed gene expression profiles for further reconstruction of gene regulatory networks are presented. A technology for removing uninformative genes based on statistical criteria and Shannon’s entropy, considering the degree of priority of the corresponding criterion, is proposed. The range of variation of the values of the input parameters within the framework of the proposed model is determined by analyzing general statistics, while for the absolute values of gene expression, the maximum value of expression for each profile is determined in the first step. Next, general statistics are formed for the obtained vector of maximum values of gene expression, vector of dispersion of gene expression profiles and Shannon entropy. To create a fuzzy model, the interquartile interval of changes in maximum absolute values, dispersion and Shannon entropy of gene expression profiles are used. At the same time, the formed ranges are divided into three intervals with corresponding terms. A fuzzy model of the formation of a subset of informative gene expression profiles is developed, the validation of which is carried out by applying a classifier to objects containing the expression values of the genes selected in the subset as attributes. The results of classification of objects containing gene expression data in selected subsets as attributes shows the high efficiency of the proposed model, since the values of the object classification criteria correspond to the level of informativeness of the corresponding group of gene expression profiles.

Further perspectives of the author’s research are the practical implementation of the proposed model for the formation of subsets of informative gene expression profiles for the purpose of reconstructing gene regulatory networks.

Keywords: gene expression, statistical criteria, Shannon entropy, fuzzy logic, clas­sification criteria, ROC analysis.

doi: 10.32403/1998-6912-2023-1-66-39-55

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