Hybrid Inductive Model of Differentially and Co-Expressed Gene Expression Profile Extraction Based on the JointUse of Clustering Technique and Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F22%3A43897173" target="_blank" >RIV/44555601:13440/22:43897173 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/12/22/11795" target="_blank" >https://www.mdpi.com/2076-3417/12/22/11795</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app122211795" target="_blank" >10.3390/app122211795</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid Inductive Model of Differentially and Co-Expressed Gene Expression Profile Extraction Based on the JointUse of Clustering Technique and Convolutional Neural Network

Popis výsledku v původním jazyce

The development of hybrid models focused on gene expression data processing for the allocation of differently expressed and mutually correlated genes is one of the current directions of modern bioinformatics. The solution of this problem can allow us to improve the effectiveness of the existing systems for complex diseases diagnosis based on gene expression data analysis on the one hand and increase the efficiency of gene regulatory network reconstruction procedure by more careful selection of genes considering the type of disease on the other hand. In this research, we propose the stepwise procedure to form the subsets of mutually correlated and differently expressed gene expression profiles (GEP). Firstly, we allocate the informative GEP in terms of statistical and entropy criteria using the Harrington desirability function. Then, we performed the cluster analysis using SOTA and spectral clustering algorithms implemented within the framework of objective clustering inductive technology. The result of this step implementation is a set of clusters containing co- and differently expressed GEP. Validation of the model was performed using a one-dimensional two-layer convolutional neural network (CNN). The analysis of the simulation results has shown the high efficiency of the proposed model. The clusters of GEP formed based on the clustering quality criteria values allowed us to identify the investigated objects with high accuracy. Moreover, the simulation results have also shown that the hybrid inductive model based on the spectral clustering algorithm is more effective in comparison with the use of the SOTA clustering algorithm in terms of both the complexity of the optimal cluster structure forming and the classification accuracy of the objects that contain the allocated gene expression data as attributes.

Název v anglickém jazyce

Hybrid Inductive Model of Differentially and Co-Expressed Gene Expression Profile Extraction Based on the JointUse of Clustering Technique and Convolutional Neural Network

Popis výsledku anglicky

The development of hybrid models focused on gene expression data processing for the allocation of differently expressed and mutually correlated genes is one of the current directions of modern bioinformatics. The solution of this problem can allow us to improve the effectiveness of the existing systems for complex diseases diagnosis based on gene expression data analysis on the one hand and increase the efficiency of gene regulatory network reconstruction procedure by more careful selection of genes considering the type of disease on the other hand. In this research, we propose the stepwise procedure to form the subsets of mutually correlated and differently expressed gene expression profiles (GEP). Firstly, we allocate the informative GEP in terms of statistical and entropy criteria using the Harrington desirability function. Then, we performed the cluster analysis using SOTA and spectral clustering algorithms implemented within the framework of objective clustering inductive technology. The result of this step implementation is a set of clusters containing co- and differently expressed GEP. Validation of the model was performed using a one-dimensional two-layer convolutional neural network (CNN). The analysis of the simulation results has shown the high efficiency of the proposed model. The clusters of GEP formed based on the clustering quality criteria values allowed us to identify the investigated objects with high accuracy. Moreover, the simulation results have also shown that the hybrid inductive model based on the spectral clustering algorithm is more effective in comparison with the use of the SOTA clustering algorithm in terms of both the complexity of the optimal cluster structure forming and the classification accuracy of the objects that contain the allocated gene expression data as attributes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2022

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

2022

Číslo periodika v rámci svazku

12(22)

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

23

Strana od-do

"nestrankovano"

Kód UT WoS článku

000887113100001

EID výsledku v databázi Scopus

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