A Hybrid Model of Cancer Diseases Diagnosis Based on Gene Expression Data with Joint Use of Data Mining Methods and Machine Learning Techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43897684" target="_blank" >RIV/44555601:13440/23:43897684 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85160859759&origin=resultslist&sort=plf-f#metrics" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85160859759&origin=resultslist&sort=plf-f#metrics</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13106022" target="_blank" >10.3390/app13106022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Hybrid Model of Cancer Diseases Diagnosis Based on Gene Expression Data with Joint Use of Data Mining Methods and Machine Learning Techniques

Popis výsledku v původním jazyce

One of the current focuses of modern bioinformatics is the development of hybrid models to process gene expression data, in order to create diagnostic systems for various diseases. In this study, we propose a solution to this problem that combines an inductive spectral clustering algorithm, random forest classifier, convolutional neural network, and alternative voting method for making the final decision about patient condition. In the first stage, we apply the spectral clustering algorithm to gene expression profiles using inductive methods of objective clustering, with the calculation of internal, external, and balance clustering quality criteria. This results in clusters of mutually correlated and differently expressed gene expression profiles. In the second stage, we apply the random forest classifier and convolutional neural network to identify the examined objects, containing as attributes the gene expression values in the allocated clusters.The presented research solves both binary- and multi-classification tasks. The final decision about the patient?s condition is made using the alternative voting method, considering the classification results based on the gene expression data in various clusters. The simulation results showed that the proposed technique was highly effective, achieving a high accuracy in object identification when both classifiers were used. However, the convolutional neural network had a significantly higher data processing efficiency than the random forest algorithm, due to its substantially shorter processing time.

Název v anglickém jazyce

A Hybrid Model of Cancer Diseases Diagnosis Based on Gene Expression Data with Joint Use of Data Mining Methods and Machine Learning Techniques

Popis výsledku anglicky

One of the current focuses of modern bioinformatics is the development of hybrid models to process gene expression data, in order to create diagnostic systems for various diseases. In this study, we propose a solution to this problem that combines an inductive spectral clustering algorithm, random forest classifier, convolutional neural network, and alternative voting method for making the final decision about patient condition. In the first stage, we apply the spectral clustering algorithm to gene expression profiles using inductive methods of objective clustering, with the calculation of internal, external, and balance clustering quality criteria. This results in clusters of mutually correlated and differently expressed gene expression profiles. In the second stage, we apply the random forest classifier and convolutional neural network to identify the examined objects, containing as attributes the gene expression values in the allocated clusters.The presented research solves both binary- and multi-classification tasks. The final decision about the patient?s condition is made using the alternative voting method, considering the classification results based on the gene expression data in various clusters. The simulation results showed that the proposed technique was highly effective, achieving a high accuracy in object identification when both classifiers were used. However, the convolutional neural network had a significantly higher data processing efficiency than the random forest algorithm, due to its substantially shorter processing time.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2023

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

13

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

"nestrankovano"

Kód UT WoS článku

000995665800001

EID výsledku v databázi Scopus

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