Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250799" target="_blank" >RIV/61989100:27240/22:10250799 - isvavai.cz</a>

Výsledek na webu

<a href="http://hcisj.com/data/file/article/2022110004/12-54.pdf" target="_blank" >http://hcisj.com/data/file/article/2022110004/12-54.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22967/HCIS.2022.12.054" target="_blank" >10.22967/HCIS.2022.12.054</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier

Popis výsledku v původním jazyce

In recent decades, microarray datasets have played an important role in triple negative breast cancer (TNBC) detection. Microarray data classification is a challenging process due to the presence of numerous redundant and irrelevant features. Therefore, feature selection becomes irreplaceable in this research field that eliminates non-required feature vectors from the system. The selection of an optimal number of features significantly reduces the NP hard problem, so a rough set-based feature selection algorithm is used in this manuscript for selecting the optimal feature values. Initially, the datasets related to TNBC are acquired from gene expression omnibuses like GSE45827, GSE76275, GSE65194, GSE3744, GSE21653, and GSE7904. Then, a robust multi-array average technique is used for eliminating the outlier samples of TNBC/non-TNBC which helps enhancing classification performance. Further, the pre-processed microarray data are fed to a rough set theory for optimal gene selection, and then the selected genes are given as the inputs to the ensemble classification technique for classifying low-risk genes (non-TNBC) and high-risk genes (TNBC). The experimental evaluation showed that the ensemble-based rough set model obtained a mean accuracy of 97.24%, which superior related to other comparative machine learning techniques.

Název v anglickém jazyce

Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier

Popis výsledku anglicky

In recent decades, microarray datasets have played an important role in triple negative breast cancer (TNBC) detection. Microarray data classification is a challenging process due to the presence of numerous redundant and irrelevant features. Therefore, feature selection becomes irreplaceable in this research field that eliminates non-required feature vectors from the system. The selection of an optimal number of features significantly reduces the NP hard problem, so a rough set-based feature selection algorithm is used in this manuscript for selecting the optimal feature values. Initially, the datasets related to TNBC are acquired from gene expression omnibuses like GSE45827, GSE76275, GSE65194, GSE3744, GSE21653, and GSE7904. Then, a robust multi-array average technique is used for eliminating the outlier samples of TNBC/non-TNBC which helps enhancing classification performance. Further, the pre-processed microarray data are fed to a rough set theory for optimal gene selection, and then the selected genes are given as the inputs to the ensemble classification technique for classifying low-risk genes (non-TNBC) and high-risk genes (TNBC). The experimental evaluation showed that the ensemble-based rough set model obtained a mean accuracy of 97.24%, which superior related to other comparative machine learning techniques.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Human-centric Computing and Information Sciences

ISSN

2192-1962

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

54

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

15

Strana od-do

nestrankovano

Kód UT WoS článku

000890282100001

EID výsledku v databázi Scopus

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