Technique of Gene Expression Profiles Selection Based on SOTA Clustering Algorithm Using Statistical Criteria and Shannon Entropy

Babichev, Sergii; Khamula, Orest; Durnyak, Bohdan; Škvor, Jiřı́

doi:10.1007/978-3-030-54215-3_2

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“…To our mind, improving the objectivity of informative gene expression data extraction can be achieved based on the application of the ensemble of data mining and machine learning techniques with subsequent decision making on the basis of quantitative quality criteria applied to evaluate the appropriate stage effectiveness [20,21]. Previous research [22][23][24] presented a partial solution to the hereinbefore described problem. In these papers, the authors presented the results of the research regarding the development of a hybrid model of gene expression profiles extraction based on the joint application of quantitative statistical criteria, Shannon entropy, the SOTA (self organizing tree algorithm) clustering algorithm and an ensemble of binary classifiers.…”

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confidence: 99%

Comparison Analysis of Gene Expression Profiles Proximity Metrics

et al. 2021

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The problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of the effectiveness of the most used metrics to estimate the gene expression profiles’ proximity, which can be used to extract the groups of informative gene expression profiles while taking into account the states of the investigated samples. Symmetry is very important in the field of both genes’ and/or proteins’ interaction since it undergirds essentially all interactions between molecular components in the GRN and extraction of gene expression profiles, which allows us to identify how the investigated biological objects (disease, state of patients, etc.) contribute to the further reconstruction of GRN in terms of both the symmetry and understanding the mechanism of molecular element interaction in a biological organism. Within the framework of our research, we have investigated the following metrics: Mutual information maximization (MIM) using various methods of Shannon entropy calculation, Pearson’s χ2 test and correlation distance. The accuracy of the investigated samples classification was used as the main quality criterion to evaluate the appropriate metric effectiveness. The random forest classifier (RF) was used during the simulation process. The research results have shown that results of the use of various methods of Shannon entropy within the framework of the MIM metric disagree with each other. As a result, we have proposed the modified mutual information maximization (MMIM) proximity metric based on the joint use of various methods of Shannon entropy calculation and the Harrington desirability function. The results of the simulation have also shown that the correlation proximity metric is less effective in comparison to both the MMIM metric and Pearson’s χ2 test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson’s χ2 test. The proposed metric was investigated within the framework of one-cluster structure effectiveness evaluation. To our mind, the main benefit of the proposed HPM is in increasing the objectivity of mutually similar gene expression profiles extraction due to the joint use of the various effective proximity metrics that can contradict with each other when they are used alone.

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