Evaluation of the gene expression profiles complex proximity metric effectiveness based on a hybrid technique of gene expression data extraction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F21%3A43896988" target="_blank" >RIV/44555601:13440/21:43896988 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-3038/paper10.pdf" target="_blank" >http://ceur-ws.org/Vol-3038/paper10.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of the gene expression profiles complex proximity metric effectiveness based on a hybrid technique of gene expression data extraction

Popis výsledku v původním jazyce

Gene expression data processing in order to develop the systems of complex diseases diagnostic or/and gene regulatory networks (GRN) reconstruction is one of the actual direction of modern bioinformatics. One of the important stages of this problem solving is an extraction of mutually correlated gene expression profiles (GEP) considering the used proximity metric. Within the framework of our research, we evaluate the complex metric of GEP proximity calculated as the combination of modified mutual information criterion and Pearson&apos;s chi-squared test using OPTICS clustering algorithm implemented using principles of the objective clustering inductive technique (OCIT). The examined objects classification accuracy was used as the main criterion to access the applied method effectiveness. The simulation results have shown that the proposed technique allows us to form an optimal GEP cluster structure in terms of maximum values of the patterns classification accuracy quality criterion.

Název v anglickém jazyce

Evaluation of the gene expression profiles complex proximity metric effectiveness based on a hybrid technique of gene expression data extraction

Popis výsledku anglicky

Gene expression data processing in order to develop the systems of complex diseases diagnostic or/and gene regulatory networks (GRN) reconstruction is one of the actual direction of modern bioinformatics. One of the important stages of this problem solving is an extraction of mutually correlated gene expression profiles (GEP) considering the used proximity metric. Within the framework of our research, we evaluate the complex metric of GEP proximity calculated as the combination of modified mutual information criterion and Pearson&apos;s chi-squared test using OPTICS clustering algorithm implemented using principles of the objective clustering inductive technique (OCIT). The examined objects classification accuracy was used as the main criterion to access the applied method effectiveness. The simulation results have shown that the proposed technique allows us to form an optimal GEP cluster structure in terms of maximum values of the patterns classification accuracy quality criterion.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 4th International Conference on Informatics &amp; Data-Driven Medicine

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

11

Strana od-do

150-160

Název nakladatele

RWTH AACHEN

Místo vydání

Aachen

Místo konání akce

Valencia

Datum konání akce

19. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000770795000016