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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

2022

Issue of the periodical within the volume

12(22)

Country of publishing house

CH - SWITZERLAND

Number of pages

23

Pages from-to

"nestrankovano"

UT code for WoS article

000887113100001

EID of the result in the Scopus database

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