Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F18%3A43893642" target="_blank" >RIV/44555601:13440/18:43893642 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.15587/1729-4061.2018.123634" target="_blank" >http://dx.doi.org/10.15587/1729-4061.2018.123634</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15587/1729-4061.2018.123634" target="_blank" >10.15587/1729-4061.2018.123634</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles

Popis výsledku v původním jazyce

We have developed a technique for the reconstruction and validation of models of gene networks based on the gene expression profiles derived in the course of DNA microchip experiments or by the method of RNA molecules sequencing. A structural block diagram is presented of a stepwise process for determining optimal parameters of the algorithm for reconstruction of a gene network that meet the optimum network topology. We proposed a comprehensive estimation criterion of a gene network topology based on the Harrington desirability function that contains network topological parameters as constituent components. The maximum value of this criterion corresponds to the optimal topology of a gene network. A technique for the validation of models of gene networks is based on a ROC analysis whose implementation implies a comparative analysis of the character of relations between relevant genes in the network on the basis of the totality of genes and gene networks based on the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the nature of interaction between genes that determine the process of functioning of a biological organism at different stages of development of complex genetic diseases for the purpose of early diagnosis and correction of a given process. It was established that the gene network reconstructed based on the correlation output algorithm is more efficient in comparison with the gene network based on the algorithm ARACNE. The weighted average of relative validation criterion for the derived models based on the correlation output algorithm is significantly greater than the corresponding value when applying the algorithm of ARACNE. This fact indicates a higher degree of compliance with the character of relations between respective genes in the network based on the totality of genes and in the networks based on gene expression profiles in the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the character of development of a biological organism at the gene level, which creates preconditions for early diagnosis and adjustment of the development of different types of genetic diseases.

Název v anglickém jazyce

Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles

Popis výsledku anglicky

We have developed a technique for the reconstruction and validation of models of gene networks based on the gene expression profiles derived in the course of DNA microchip experiments or by the method of RNA molecules sequencing. A structural block diagram is presented of a stepwise process for determining optimal parameters of the algorithm for reconstruction of a gene network that meet the optimum network topology. We proposed a comprehensive estimation criterion of a gene network topology based on the Harrington desirability function that contains network topological parameters as constituent components. The maximum value of this criterion corresponds to the optimal topology of a gene network. A technique for the validation of models of gene networks is based on a ROC analysis whose implementation implies a comparative analysis of the character of relations between relevant genes in the network on the basis of the totality of genes and gene networks based on the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the nature of interaction between genes that determine the process of functioning of a biological organism at different stages of development of complex genetic diseases for the purpose of early diagnosis and correction of a given process. It was established that the gene network reconstructed based on the correlation output algorithm is more efficient in comparison with the gene network based on the algorithm ARACNE. The weighted average of relative validation criterion for the derived models based on the correlation output algorithm is significantly greater than the corresponding value when applying the algorithm of ARACNE. This fact indicates a higher degree of compliance with the character of relations between respective genes in the network based on the totality of genes and in the networks based on gene expression profiles in the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the character of development of a biological organism at the gene level, which creates preconditions for early diagnosis and adjustment of the development of different types of genetic diseases.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Eastern-European Journal of Enterprise Technologies

ISSN

1729-3774

e-ISSN

—

Svazek periodika

2018

Číslo periodika v rámci svazku

1/4 (91)

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

19-32

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85042799438