Comparison Analysis of the Pearson's Phi-Square Test and Correlation Metric Effectiveness to Form the Subset of Differently Expressed and Mutually Correlated Genes

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Comparison Analysis of the Pearson's Phi-Square Test and Correlation Metric Effectiveness to Form the Subset of Differently Expressed and Mutually Correlated Genes

Popis výsledku v původním jazyce

The development of patients&apos; health monitoring systems based on gene expression data is a very important direction of current bioinformatics. In this instance, the allocation of both differently expressed and mutually correlated gene expression profiles (GEP) which allow monitoring in real-time the patients&apos; health with high accuracy is a very important step of this problem solution. There are various types of similarity metrics to identify the level of GEP proximity. In this research, we compare the Pearson chi-square test and correlation metric to evaluate the gene expression profiles proximity. The evaluation of appropriate metric effectiveness has been executed by applying the object&apos;s classification quality criteria such as accuracy, f-score and Matthews correlation coefficient (MCC). The simulation results have shown that the metric based on Pearson&apos;s phi-square coefficient is significantly effective in comparison with the correlation metric to allocate the mutually similar gene expression profiles and, this metric can be used when the differently expressed and mutually correlated GEP will be extracted using various clustering algorithms

Název v anglickém jazyce

Comparison Analysis of the Pearson's Phi-Square Test and Correlation Metric Effectiveness to Form the Subset of Differently Expressed and Mutually Correlated Genes

Popis výsledku anglicky

The development of patients&apos; health monitoring systems based on gene expression data is a very important direction of current bioinformatics. In this instance, the allocation of both differently expressed and mutually correlated gene expression profiles (GEP) which allow monitoring in real-time the patients&apos; health with high accuracy is a very important step of this problem solution. There are various types of similarity metrics to identify the level of GEP proximity. In this research, we compare the Pearson chi-square test and correlation metric to evaluate the gene expression profiles proximity. The evaluation of appropriate metric effectiveness has been executed by applying the object&apos;s classification quality criteria such as accuracy, f-score and Matthews correlation coefficient (MCC). The simulation results have shown that the metric based on Pearson&apos;s phi-square coefficient is significantly effective in comparison with the correlation metric to allocate the mutually similar gene expression profiles and, this metric can be used when the differently expressed and mutually correlated GEP will be extracted using various clustering algorithms

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

CEUR Workshop Proceedings

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

11

Strana od-do

93-102

Název nakladatele

CEUR-WS

Místo vydání

Germany

Místo konání akce

Khmelnick

Datum konání akce

23. 3. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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