Abstract
Gene Expression (GE) data have been attracting researchers since ages by virtue of the essential genetic information they carry, that plays a pivotal role in both causing and curing terminal ailments. GE data are generated using DNA microarrays. These gene expression data are obtained in measurements of thousands of genes with relatively very few samples. The main challenge in analyzing microarray gene data is not only in finding differentially expressed genes, but also in applying computational methods to the increasing size of microarray gene expression data. This review will focus on gene selection approaches for simultaneous exploratory analysis of multiple cancer datasets. The authors provide a brief review of several gene selection algorithms and the principle behind selecting a suitable gene selection algorithm for extracting predictive genes for cancer prediction. The performance has been evaluated using 10-fold Average Split accuracy method. As microarray gene data is growing massively in volume, the computational methods need to be scalable to explore and process such massive datasets. Moreover, it consumes more time, labour and cost when this investigation is done in serial (sequential) manner. This motivated the authors to propose parallelized gene selection and classification approach for selecting optimal genes and categorizing the cancer subtypes. The authors also present the hurdles faced in adopting parallelized computational methods for microarray gene data while substantiating the need for parallel techniques by evaluating their performance with previously reported research in this sphere of study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jacob, S.G., and R.G. Ramani. 2012. Data mining in clinical data sets: a review training. International Journal of Applied Information Systems 4 (6): 15–26.
Piatetsky-Shapiro, G., and P. Tamayo. 2003. Microarray data mining: Facing the challenges. ACM SIGKDD Explorations Newsletter 5 (2): 1–5.
Golub, T.R., D.K. Slonim, P. Tamayo, et al. 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (5439): 531–537.
Liu, H., R.G. Sadygov, and J.R. Yates. 2004. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Analytical Chemistry 76 (14): 4193–4201.
Helleputte, T., and P. Dupont. 2009. Feature selection by transfer learning with linear regularized models. In Joint European conference on machine learning and knowledge discovery in databases, 533–547. Berlin Heidelberg: Springer.
Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research: 1157–1182.
Guan, P., D. Huang, M. He, et al. 2009. Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method. Journal of Experimental and Clinical Cancer Research 28 (1): 1–7.
Rangarajan, L. 2010. Bi-level dimensionality reduction methods using feature selection and feature extraction. International Journal of Computer Applications. 4 (2): 33–38.
Gracia Jacob, S. 2015. Discovery of novel oncogenic patterns using hybrid feature selection and rule mining. Ph.D. thesis. Anna University.
Han, J., and Micheline, Kamber. 2006. Data mining concepts and techniques, 2nd ed. Elsevier.
Jirapech-Umpai, T., and S. Aitken. 2005. Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 6 (1): 1–11.
Masih, S., and S. Tanwani. 2014. Data mining techniques in parallel and distributed environment-a comprehensive survey. International Journal of Emerging Technology and Advanced Engineering 4 (3): 453–461.
Pakize, S.R., and A. Gandomi. 2014. Comparative study of classification algorithms based on MapReduce model. International Journal of Innovative Research in Advanced Engineering: 2349–2163.
Parallel Programming Framework Apache Spark. http://spark.apache.org/. Accessed 9 Nov 2016.
Meng, X., J. Bradley, B. Yuvaz, et al. 2016. Mllib: Machine learning in apache spark. Journal of Machine Learning Research. 17 (34): 1–7.
Hall, M., E. Frank, G. Holmes, & I.H. Witten et al. 2009. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11 (1): 10–18.
Parallel Programming Framework Spark. Machine Learning Library (SparkMLlib). http://spark.apache.org/docs/latest/mllib-guide.html. Accessed 6 Nov 2016.
Artificial Intelligence Orange Labs. Ljubljana. http://www.biolab.si/supp/bi-cancer/projections/. Accessed 31 Oct 2016.
Hall, M. 1999. Correlation-based feature selection for machine learning. Ph.D. thesis.
Kuncheva, L.I. 1992. Fuzzy rough sets: Application to feature selection. Fuzzy Sets and Systems 51 (2): 147–153.
Geng, X., T.Y. Liu, T. Qin et al. 2007. Feature selection for ranking. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, 407–414.
Shannon, C.E. 2001. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5 (1): 3–55.
Karegowda, A.G., A.S. Manjunath, and M.A. Jayaram. 2010. Comparative study of attribute selection using gain ratio and correlation based feature selection. International Journal of Information Technology and Knowledge Management 2 (2): 271–277.
Jiang, B.N., X.Q. Ding, L.T. Ma, et al. 2008. A hybrid feature selection algorithm: Combination of symmetrical uncertainty and genetic algorithms. In The second international symposium on optimization and systems biology, 152–157.
Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research: 1289–305.
Kira, K., and L.A. Rendell. 1992. A practical approach to feature selection. In Proceedings of the ninth international workshop on Machine learning, 249–256.
Alonso-González, C.J., Q.I. Moro-Sancho, A. Simon-Hurtado, et al. 2012. Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods. Expert Systems with Applications 39 (8): 7270–7280.
Zhang, H., L. Li, C. Luo, et al. 2014. Informative gene selection and direct classification of tumor based on chi-square test of pairwise gene interactions. BioMed Research International 2014: 1–10.
Begum, S., D. Chakraborty, and R. Sarkar. 2015. Cancer classification from gene expression based microarray data using SVM ensemble. In 2015 International conference on condition assessment techniques in electrical systems (CATCON), 13–16. IEEE.
Jeyachidra, J., and M. Punithavalli. 2013. A comparative analysis of feature selection algorithms on classification of gene microarray dataset. In Information communication and embedded systems (ICICES), IEEE 2013 international conference on 2013, 1088–1093.
Weitschek, E., G. Fiscon, G. Felici, et al. 2015. Gela: A software tool for the analysis of gene expression data. In 2015 26th international workshop on database and expert systems applications (DEXA) IEEE, 31–35.
Cabrera, J., A. Dionisio, G. Solano. 2015. Lung cancer classification tool using microarray data and support vector machines. In Information, Intelligence, Systems and Applications (IISA), 2015 6th International Conference. IEEE, 1–6.
Nguyen, C., Y. Wang, and H.N. Nguyen. 2013. Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering. 6 (5): 551–560.
Rajeswari, K., V. Vaithiyanathan, and S.V. Pede. 2013. Feature selection for classification in medical data mining. International Journal of Emerging Trends and Technology in Computer Science (IJETTCS). 2 (2): 492–497.
Lavanya, D., and K.U. Rani. 2012. Ensemble decision tree classifier for breast cancer data. International Journal of Information Technology Convergence and Services. 2 (1): 17–24.
Ben-Dor, A., L. Bruhn, N. Friedman, et al. 2000. Tissue classification with gene expression profiles. Journal of Computational Biology 7 (3–4): 559–583.
Hassanien, A.E. 2003. Classification and feature selection of breast cancer data based on decision tree algorithm. Studies in Informatics and Control. 12 (1): 33–40.
Kashyap, H., H.A. Ahmed, N. Hoque, et al. 2015. Big data analytics in bioinformatics: A machine learning perspective. arXiv preprint arXiv:1506.05101. 13 (9): 1–20.
Stokes, T.H., R.A. Moffitt, J.H. Phan, et al. 2007. chip artifact CORRECTion (caCORRECT): a bioinformatics system for quality assurance of genomics and proteomics array data. Annals of Biomedical Engineering 35 (6): 1068–1080.
Phan, J.H., A.N. Young, and M.D. Wang. 2013. omniBiomarker: a web-based application for knowledge-driven biomarker identification. IEEE Transactions on Biomedical Engineering 60 (12): 3364–3367.
Li. M., J. Tan, Y. Wang, et al. 2015. Sparkbench: A comprehensive benchmarking suite for in memory data analytic platform spark. In Proceedings of the 12th ACM international conference on computing frontiers, vol. 53, 1–8.
Koliopoulos, A.K., P. Yiapanis, F. Tekiner, et. al. A parallel distributed weka framework for big data mining using spark. In 2015 IEEE international congress on big data, 9–16.
Shafer, J., R. Agrawal, and M. Mehta. 1996. SPRINT: A scalable parallel classifier for data mining. In Proceeding of the 1996 international conference, 544–555. Very Large Data Bases.
Chauhan, H., and A. Chauhan. 2013. Implementation of decision tree algorithm c4. International Journal of Scientific and Research Publications 3 (10): 1–3.
Wakayama, R., R. Murata, A. Kimura, et al. 2015. Distributed forests for MapReduce-based machine learning. In Proceedings of the IAPR Asian conference on pattern recognition (ACPR), 1–5.
Han, J., Y. Liu, and X. Sun. A scalable random forest algorithm based on MapReduce. In Software engineering and service science (ICSESS), 2013 4th IEEE international conference on 2013, 849–852.
Li, B., X. Chen, M. J. Li, et al. 2012. Scalable random forests for massive data. In Pacific-Asia conference on knowledge discovery and data mining, 135–146. Springer Berlin Heidelberg.
Hall, L.O., N. Chawla, and K.W. Bowyer. 1998. Combining decision trees learned in parallel. In Working notes of the KDD-97 workshop on distributed data mining, 10–15.
Amado, N., J. Gama, and F. Silva. 2004. Exploiting parallelism in decision tree induction. In Proceedings from the ECML/PKDD workshop on parallel and distributed computing for machine learning, 13–22.
Richards JW, Eads D, Bloom JS, Brink H, Starr D. WiseRFTM: A fast and scalable Random Forest. A WHITE PAPER from wise.io. 2013.
Islam, A.T., B.S. Jeong, A.G. Bari, et al. 2015. MapReduce based parallel gene selection method. Applied Intelligence 42 (2): 147–156.
Peralta, D., S. del RÃo, S. RamÃrez-Gallego, et al. 2015. Evolutionary feature selection for big data classification: A mapreduce approach. Mathematical Problems in Engineering 2015: 1–11.
Wang, X., and O. Gotoh. 2010. A robust gene selection method for microarray-based cancer classification. Cancer Informatics 9: 15–30.
Wu, G., H. Li, X. Hu, et al. 2009. MReC4. 5: C4. 5 ensemble classification with MapReduce. In 2009 fourth ChinaGrid annual conference, 249–255. IEEE.
Wu, Z., Y. Li, A. Plaza, et al. 2016. Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (6): 2270–2278.
Ramani, R.G., and S.G. Jacob. 2013. Benchmarking classification models for cancer prediction from gene expression data: A novel approach and new findings. Studies Informatics Control 22 (2): 134–143.
Das, H., B. Naik, and H.S. Behera. 2018. Classification of diabetes mellitus disease (DMD): A data mining (DM) approach. In Progress in computing, analytics and networking, 539–549. Singapore: Springer.
Das, H., A.K. Jena, J. Nayak, B. Naik, and H.S. Behera. 2015. A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In Computational intelligence in data mining, vol. 2, 461–471. New Delhi: Springer.
Sahoo, A.K., S. Mallik, C. Pradhan, B.S. Mishra, R.K. Barik, and H. Das. 2019. Intelligence-based health recommendation system using big data analytics. In In big data analytics for intelligent healthcare management, 227–246. Academic Press.
Dey, N., H. Das, B. Naik, & H.S. Behera (Eds.). 2019. Big data analytics for intelligent healthcare management. Academic Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Venkataramana, L., Jacob, S.G., Saraswathi Shanmuganathan, Venkata Vara Prasad Dattuluri (2020). Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study. In: Rout, M., Rout, J., Das, H. (eds) Nature Inspired Computing for Data Science. Studies in Computational Intelligence, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-33820-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-33820-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33819-0
Online ISBN: 978-3-030-33820-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)