Extreme learning machine for cancerclassification from miRNA gene expression data
Paper Details
Extreme learning machine for cancerclassification from miRNA gene expression data
Abstract
Cancer classification from microRNA (miRNA) gene expression data is a difficult task in system biology and machine learning as conventional classification methods require a sufficiently large number of labeled samples to train the classifiers accurately, particularly when the labeled samples are very expensive and difficult to collect. Therefore, conventional classification methods usually do not provide the desired classification accuracy due to the scarcity of training samples. In this context, we present an extreme learning machine (ELM) technique for cancer classification from miRNA gene expression data that can improve the classification accuracy as it is extremely fast and accurate compared to other traditional methods. The presented method is evaluated using publicly available miRNA gene expression datasets of breast cancer, pancreatic cancer, colorectal cancer, prostate cancer and lung cancer in terms of classification accuracy, precision, recall, macro F1-measure, micro F1-measure and kappa in comparison to four other state-of-the-art methods. Experimental results justify the dominance of the ELM method over the other compared methods for cancer sample classification from miRNA Gene Expression data.
Aha DW, Kibler D, Albert MK. 1991. Instance-Based Learning Algorithms. Machine Learning 6, 37–66. https://doi.org/10.1007/BF00153759
Akusok A, Bjrk K, Miche Y, Lendasse A. 2015. High-performance extreme learning machines: A complete toolbox for big data applications. IEEE Access 3, 1011–1025. https://doi.org/10.1109/ACCESS.2015.2450498
Chandra B, Gupta M. 2011. Robust approach for estimating probabilities in naïve Bayesian classifier for gene expression data. Expert Systems with Applications 38(3), 1293-1298. https://doi.org/10.1016/j.eswa.2010.06.076
Chen X, Ishwaran H. 2012. Random forests for genomic data analysis. Genomics 99(6), 323–329. https://doi.org/10.1016/j.ygeno.2012.04.003
Clough E, Barrett T. 2016. The gene expression omnibus database. Statistical Genomics: Methods in Molecular Biology, Humana Press, New York 1418, 93–110. https://doi.org/10.1007/978-1-4939-3578-9_5
Cohen J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46. https://doi.org/10.1177/001316446002000104
Ding S, Zhao H, Zhang Y, Xu X, ru N. 2013. Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review 44(06), 1–8. https://doi.org/10.1007/s10462-013-9405-z
Esquela-Kerscher E, Slack FJ. 2006. Oncomirs-micro RNAs with a role in cancer. Nature reviews cancer 6(4), 259–269. https://doi.org/10.1038/nrc1840
Haider AA, Asghar S. 2013. A survey of logic-based classifiers. International Journal of Future Computer and Communication 2(2), 126–129. https://doi.org/10.7763/IJFCC.2013.V2.135
Halder A, Misra S. 2014. Semi-supervised fuzzy k-nn for cancer classification from microarray gene expression data. In: 1st International Conference on Automation, Control, Energy and Systems (ACES 2014) (IEEE Computer Society Press). https://doi.org/10.1109/ACES.2014.6808013
Halder A, Kumar A. 2019. Active learning using rough fuzzy classifier for cancer predication from microarray gene expression data. Journal of Biomedical Informatics 92, p 103136. https://doi.org/10.1016/j.jbi.2019.103136
Huang G, Zhu Q, Siew C. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70(1), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Huang G, Huang GB, Song S, You K. 2015. Trends in extreme learning machines: a review. Neural Networks 61, 32–48. https://doi.org/10.1016/j.neunet.2014.10.001
Hwang HW, Mendell JT. 2006. Micrornas in cell proliferation, cell death, and tumorigenesis. British journal of cancer 96(6), 776–780. https://doi.org/10.1038/sj.bjc.6603023
Kumar A, Halder A. 2019. Active learning using fuzzy-rough nearest neighbour classifier for cancer prediction from microarray gene expression data. International Journal of Pattern Recognition and Artificial Intelligence 34(1), p. 2057001. https://doi.org/10.1142/S0218001420570013
Kumar A, Halder A. 2020. Ensemble-based active learning using fuzzy-rough approach for cancer sample classification. Engineering Applications of Artificial Intelligence 91, p. 103591. https://doi.org/10.1016/j.engappai.2020.103591
Marak DCB, Halder A, Kumar A. 2021. Semi-supervised ensemble learning for efficient cancer sample classification from miRNA gene expression data. New Generation Computing 39, 487–513. https://doi.org/10.1007/s00354-021-00123-5
Pirooznia M, Yang J, Yang MQ, Deng Y. 2008. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics 9(1), 1–13. https://doi.org/10.1186/1471-2164-9-S1-S13
Sung H, Ferlay J, Siegel R L, Laversanne M, Soerjomataram I. 2021. A. Jemal and F. Bray, Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 71(3), 209–249. https://doi.org/10.3322/caac.21660
Tarek S, El-Khoribi R, Shoman M. 2017. Gene expression-based cancer classification. Egyptian Informatics Journal 18(3), 151–159. https://doi.org/10.1016/j.eij.2016.12.001
Vanitha CDA, Devaraj D, Venkatesulu M. 2015. Gene expression data classification using support vector machine and mutual information-based gene selection. Procedia Computer Science 47, 13–21. https://doi.org/10.1016/j.procs.2015.03.178
Ansuman Kumar, Anindya Halder (2022), Extreme learning machine for cancerclassification from miRNA gene expression data; IJB, V20, N5, May, P169-175
https://innspub.net/extreme-learning-machine-for-cancerclassification-from-mirna-gene-expression-data/
Copyright © 2022
By Authors and International
Network for Natural Sciences
(INNSPUB) https://innspub.net
This article is published under the terms of the
Creative Commons Attribution License 4.0