Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Human-centric Computing and Information Sciences
ISSN
2192-1962
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
54
Country of publishing house
KR - KOREA, REPUBLIC OF
Number of pages
15
Pages from-to
nestrankovano
UT code for WoS article
000890282100001
EID of the result in the Scopus database
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