Comparison analysis of gene expression profiles proximity metrics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F21%3A43897025" target="_blank" >RIV/44555601:13440/21:43897025 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-8994/13/10/1812" target="_blank" >https://www.mdpi.com/2073-8994/13/10/1812</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym13101812" target="_blank" >10.3390/sym13101812</a>

Result language

angličtina

Original language name

Comparison analysis of gene expression profiles proximity metrics

Original language description

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 ?2 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 ?2 test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson?s ?2 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

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Symmetry

ISSN

2073-8994

e-ISSN

2073-8994

Volume of the periodical

2021

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

"nestrankovano"

UT code for WoS article

000712641200001

EID of the result in the Scopus database

2-s2.0-85116102768