Efficient Gene Expression Data Analysis using ES-DBN For Microarray Cancer Data Classification





Microarrays, Gene expression, cancer classification, Pearson Correlation Coefficient based GloVe (PCC- GloVe), Categorical columns, Exponential Sigmoid - Deep Belief Network (ES-DBN), Cauchy Mutation-Coral Reefs Optimization (CM-CRO)


INTRODUCTION: DNA microarray has become a promising means for classification of various cancer types via the creation of various Gene Expression (GE) profiles, with the advancement of technologies. But, it is challenging to classify the GE profile since not all genes contribute to the presence of cancer and might lead to incorrect diagnoses. Thus an efficient GE data analysis for microarray cancer data classification using Exponential Sigmoid-Deep Belief Network (ES-DBN) is proposed in this work.

OBJECTIVES: The study aims to develop an efficient GE data analysis using Exponential Sigmoid-Deep Belief Network (ES-DBN) for microarray cancer data classification.

METHODS: The proposed methodology starts with pre-processing to compact data. Afterward, by utilizing Min-Max feature scaling technique, the pre-processed data is normalized. The normalized data is further encoded and feature  ranking is performed. The subset values are selected using Cauchy Mutation-Coral Reefs Optimization (CM-CRO) in feature ranking. The feature vector is calculated by Pearson Correlation Coefficient based GloVe (PCC-GloVe) algorithm since different subsets return the same fitness value. Statistical and Biological validations take place after feature vector calculation. Lastly, for effective classification of the type of cancer, the vector features obtained are fed to ES-DBN.

RESULTS: The outcomes of the proposed technique are evaluated with various datasets, which exhibited that the proposed technique performed well with the Ovarian cancer dataset and outperforms other conventional approaches.

CONCLUSION: This study presents a comprehensive methodology for efficiently classifying cancer types using GE profile. The proposed GE data analysis using ES-DBN shows promising results, highlighting its potential as a valuable tool for cancer diagnosis and classification.


Download data is not yet available.


Q. Liao, Y. Ding, Z. L. Jiang, X. Wang, C. Zhang, and Q. Zhang, “Multi-task deep convolutional neural network for cancer diagnosis,” Neurocomputing, vol. 348, pp. 66–73, 2019, doi: 10.1016/j.neucom.2018.06.084. DOI: https://doi.org/10.1016/j.neucom.2018.06.084

M. Ghosh, S. Begum, R. Sarkar, D. Chakraborty, and U. Maulik, “Recursive Memetic Algorithm for gene selection in microarray data,” Expert Syst. Appl., vol. 116, pp. 172–185, 2019, doi: 10.1016/j.eswa.2018.06.057. DOI: https://doi.org/10.1016/j.eswa.2018.06.057

M. Mostavi, Y. C. Chiu, Y. Huang, and Y. Chen, “Convolutional neural network models for cancer type prediction based on gene expression,” BMC Med. Genomics, vol. 13, no. Suppl 5, pp. 1–13, 2020, doi: 10.1186/s12920-020-0677-2. DOI: https://doi.org/10.1186/s12920-020-0677-2

G. W. Wright et al., “A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications,” Cancer Cell, vol. 37, no. 4, pp. 551-568.e14, 2020, doi: 10.1016/j.ccell.2020.03.015. DOI: https://doi.org/10.1016/j.ccell.2020.03.015

Y. Huo, L. Xin, C. Kang, M. Wang, Q. Ma, and B. Yu, “SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso,” J. Theor. Biol., vol. 486, p. 110098, 2020, doi: 10.1016/j.jtbi.2019.110098. DOI: https://doi.org/10.1016/j.jtbi.2019.110098

A. Lopez-Rincon, M. Martinez-Archundia, G. U. Martinez-Ruiz, A. Schoenhuth, and A. Tonda, “Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection,” BMC Bioinformatics, vol. 20, no. 1, pp. 1–17, 2019, doi: 10.1186/s12859-019-3050-8. DOI: https://doi.org/10.1186/s12859-019-3050-8

B. H. Shekar and G. Dagnew, “Grid search-based hyperparameter tuning and classification of microarray cancer data,” 2019 2nd Int. Conf. Adv. Comput. Commun. Paradig. ICACCP 2019, pp. 1–8, 2019, doi: 10.1109/ICACCP.2019.8882943. DOI: https://doi.org/10.1109/ICACCP.2019.8882943

M. Daoud and M. Mayo, “A survey of neural network-based cancer prediction models from microarray data,” Artif. Intell. Med., vol. 97, pp. 204–214, 2019, doi: 10.1016/j.artmed.2019.01.006. DOI: https://doi.org/10.1016/j.artmed.2019.01.006

S. P. Potharaju and M. Sreedevi, “Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance,” Clin. Epidemiol. Glob. Heal., vol. 7, no. 2, pp. 171–176, 2019, doi: 10.1016/j.cegh.2018.04.001. DOI: https://doi.org/10.1016/j.cegh.2018.04.001

A. K. Shukla, D. Tripathi, B. R. Reddy, and D. Chandramohan, “A study on metaheuristics approaches for gene selection in microarray data: algorithms, applications and open challenges,” Evol. Intell., vol. 13, no. 3, pp. 309–329, 2020, doi: 10.1007/s12065-019-00306-6. DOI: https://doi.org/10.1007/s12065-019-00306-6

H. Lu, H. Gao, M. Ye, and X. Wang, “A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 18, no. 3, pp. 863–870, 2021, doi: 10.1109/TCBB.2019.2952102. DOI: https://doi.org/10.1109/TCBB.2019.2952102

A. K. Shukla, P. Singh, and M. Vardhan, “A new hybrid wrapper TLBO and SA with SVM approach for gene expression data,” Inf. Sci. (Ny)., vol. 503, pp. 238–254, 2019, doi: 10.1016/j.ins.2019.06.063. DOI: https://doi.org/10.1016/j.ins.2019.06.063

A. Sampathkumar, R. Rastogi, S. Arukonda, A. Shankar, S. Kautish, and M. Sivaram, “An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 4743–4751, 2020, doi: 10.1007/s12652-020-01731-7. DOI: https://doi.org/10.1007/s12652-020-01731-7

Z. Y. Algamal and M. H. Lee, “A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification,” Adv. Data Anal. Classif., vol. 13, no. 3, pp. 753–771, 2019, doi: 10.1007/s11634-018-0334-1. DOI: https://doi.org/10.1007/s11634-018-0334-1

L. Sun, X. Y. Zhang, Y. H. Qian, J. C. Xu, S. G. Zhang, and Y. Tian, “Joint neighborhood entropy-based gene selection method with fisher score for tumor classification,” Appl. Intell., vol. 49, no. 4, pp. 1245–1259, 2019, doi: 10.1007/s10489-018-1320-1. DOI: https://doi.org/10.1007/s10489-018-1320-1

S. Sayed, M. Nassef, A. Badr, and I. Farag, “A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets,” Expert Syst. Appl., vol. 121, pp. 233–243, 2019, doi: 10.1016/j.eswa.2018.12.022. DOI: https://doi.org/10.1016/j.eswa.2018.12.022

T. K. B. Mudiyanselage, X. Xiao, Y. Zhang, and Y. Pan, “Deep Fuzzy Neural Networks for Biomarker Selection for Accurate Cancer Detection,” IEEE Trans. Fuzzy Syst., vol. 28, no. 12, pp. 3219–3228, 2020, doi: 10.1109/TFUZZ.2019.2958295. DOI: https://doi.org/10.1109/TFUZZ.2019.2958295




https://www.kaggle.com/code/docxian/colorectal-cancer-gene-expression-data-prep-eda/data -







How to Cite

Sucharita S, Sahu B, Swarnkar T. Efficient Gene Expression Data Analysis using ES-DBN For Microarray Cancer Data Classification. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 May 28 [cited 2024 Jul. 13];10. Available from: https://publications.eai.eu/index.php/phat/article/view/6187