Formation of Subsets of Co-expressed Gene Expression Profiles Based on Joint Use of Fuzzy Inference System,Statistical Criteria and Shannon Entropy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43897055" target="_blank" >RIV/44555601:13440/23:43897055 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-16203-9_2" target="_blank" >http://dx.doi.org/10.1007/978-3-031-16203-9_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-16203-9_2" target="_blank" >10.1007/978-3-031-16203-9_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Formation of Subsets of Co-expressed Gene Expression Profiles Based on Joint Use of Fuzzy Inference System,Statistical Criteria and Shannon Entropy

Popis výsledku v původním jazyce

The paper presents the results of the research regarding the application of a fuzzy logic inference system to form the co-expressed gene expression profiles based on the joint use of Shannon entropy and statistical criteria. The allocation of co-expressed genes can allow us to increase the disease diagnosis accuracy on the one hand and, reconstruct the qualitativegene regulatory networks on the other hand. To solve this problem, we have proposed the joint use of the fuzzy logicinference system and random forest classifier. In beginning, we have calculated for each of the gene expression profiles themaximum expression values, variance and Shannon entropy. These parameters were used as the input ones for the fuzzylogic inference system. After setting the fuzzy membership functions for both the input and output parameters, the modelformalization including fuzzy rules formation, we have applied the model to gene expression data which included initially the54675 genes for 156 patients examined at the early stage of lung cancer. As a result of this step implementation, we haveobtained the four subsets of gene expression profiles with various significance levels. To confirm the obtained results, wehave applied the classification procedure to investigated samples that included as the attributes the allocated genes. Theanalysis of the classification quality criteria allows us to conclude about the high effectiveness of the proposed technique tosolve this type of task.

Název v anglickém jazyce

Formation of Subsets of Co-expressed Gene Expression Profiles Based on Joint Use of Fuzzy Inference System,Statistical Criteria and Shannon Entropy

Popis výsledku anglicky

The paper presents the results of the research regarding the application of a fuzzy logic inference system to form the co-expressed gene expression profiles based on the joint use of Shannon entropy and statistical criteria. The allocation of co-expressed genes can allow us to increase the disease diagnosis accuracy on the one hand and, reconstruct the qualitativegene regulatory networks on the other hand. To solve this problem, we have proposed the joint use of the fuzzy logicinference system and random forest classifier. In beginning, we have calculated for each of the gene expression profiles themaximum expression values, variance and Shannon entropy. These parameters were used as the input ones for the fuzzylogic inference system. After setting the fuzzy membership functions for both the input and output parameters, the modelformalization including fuzzy rules formation, we have applied the model to gene expression data which included initially the54675 genes for 156 patients examined at the early stage of lung cancer. As a result of this step implementation, we haveobtained the four subsets of gene expression profiles with various significance levels. To confirm the obtained results, wehave applied the classification procedure to investigated samples that included as the attributes the allocated genes. Theanalysis of the classification quality criteria allows us to conclude about the high effectiveness of the proposed technique tosolve this type of task.

Druh

D - Stať ve sborníku

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes on Data Engineering and Communications Technologies

ISBN

978-3-031-16202-2

ISSN

2367-4512

e-ISSN

—

Počet stran výsledku

17

Strana od-do

25-41

Název nakladatele

Springer Nature

Místo vydání

Basel

Místo konání akce

Zalizniy Port

Datum konání akce

23. 5. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—