Feature selection and classification approaches in gene expression of breast cancer

Sarada Ghosh; Guruprasad Samanta; Manuel De la Sen; Sarada Ghosh; Guruprasad Samanta; Manuel De la Sen

doi:10.3934/biophy.2021029

AIMS Biophysics

2021, Volume 8, Issue 4: 372-384. doi: 10.3934/biophy.2021029

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Feature selection and classification approaches in gene expression of breast cancer

1.
Department of Statistics, Gurudas College, Phool Bagan, Kolkata-700054, India
2.
Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711103, India
3.
Institute of Research and Development of Processes, University of the Basque Country, 48940 Leioa, Bizkaia, Spain

Received: 20 September 2021 Accepted: 23 November 2021 Published: 01 December 2021

DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on R-software.
- breast cancer,
- random forest,
- decision tree,
- principal component analysis
Citation: Sarada Ghosh, Guruprasad Samanta, Manuel De la Sen. Feature selection and classification approaches in gene expression of breast cancer[J]. AIMS Biophysics, 2021, 8(4): 372-384. doi: 10.3934/biophy.2021029

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Abstract

DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on R-software.

Acknowledgments

The authors are grateful to the learned reviewers and Editors for their careful reading, valuable comments and helpful suggestions, which have helped them to improve the presentation of this work significantly. The third author (Manuel De la Sen) is grateful to the Spanish Government for its support through grant RTI2018-094336-B-I00 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19.

Data availability statement

The data used to support the findings of this study are included in the references within the article.

Conflict of interest

The authors declare that they have no conflict of interest regarding this work.

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