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Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 871))

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.

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References

  1. 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.

    Article  Google Scholar 

  2. Piatetsky-Shapiro, G., and P. Tamayo. 2003. Microarray data mining: Facing the challenges. ACM SIGKDD Explorations Newsletter 5 (2): 1–5.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Google Scholar 

  6. Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research: 1157–1182.

    Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Rangarajan, L. 2010. Bi-level dimensionality reduction methods using feature selection and feature extraction. International Journal of Computer Applications. 4 (2): 33–38.

    Article  Google Scholar 

  9. Gracia Jacob, S. 2015. Discovery of novel oncogenic patterns using hybrid feature selection and rule mining. Ph.D. thesis. Anna University.

    Google Scholar 

  10. Han, J., and Micheline, Kamber. 2006. Data mining concepts and techniques, 2nd ed. Elsevier.

    Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. 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.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. Parallel Programming Framework Apache Spark. http://spark.apache.org/. Accessed 9 Nov 2016.

  15. Meng, X., J. Bradley, B. Yuvaz, et al. 2016. Mllib: Machine learning in apache spark. Journal of Machine Learning Research. 17 (34): 1–7.

    MathSciNet  MATH  Google Scholar 

  16. 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.

    Google Scholar 

  17. Parallel Programming Framework Spark. Machine Learning Library (SparkMLlib). http://spark.apache.org/docs/latest/mllib-guide.html. Accessed 6 Nov 2016.

  18. Artificial Intelligence Orange Labs. Ljubljana. http://www.biolab.si/supp/bi-cancer/projections/. Accessed 31 Oct 2016.

  19. Hall, M. 1999. Correlation-based feature selection for machine learning. Ph.D. thesis.

    Google Scholar 

  20. Kuncheva, L.I. 1992. Fuzzy rough sets: Application to feature selection. Fuzzy Sets and Systems 51 (2): 147–153.

    Article  MathSciNet  Google Scholar 

  21. 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.

    Google Scholar 

  22. Shannon, C.E. 2001. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5 (1): 3–55.

    Article  MathSciNet  Google Scholar 

  23. 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.

    Google Scholar 

  24. 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.

    Google Scholar 

  25. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research: 1289–305.

    Google Scholar 

  26. 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.

    Chapter  Google Scholar 

  27. 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.

    Article  Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Google Scholar 

  30. 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.

    Google Scholar 

  31. 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.

    Google Scholar 

  32. 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.

    Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. 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.

    Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. 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.

    Article  Google Scholar 

  37. 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.

    Google Scholar 

  38. 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.

  39. 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.

    Article  Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. 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.

    Google Scholar 

  42. 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.

    Google Scholar 

  43. 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.

    Google Scholar 

  44. Chauhan, H., and A. Chauhan. 2013. Implementation of decision tree algorithm c4. International Journal of Scientific and Research Publications 3 (10): 1–3.

    Google Scholar 

  45. 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.

    Google Scholar 

  46. 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.

    Google Scholar 

  47. 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.

    Chapter  Google Scholar 

  48. 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.

    Google Scholar 

  49. 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.

    Google Scholar 

  50. Richards JW, Eads D, Bloom JS, Brink H, Starr D. WiseRFTM: A fast and scalable Random Forest. A WHITE PAPER from wise.io. 2013.

    Google Scholar 

  51. Islam, A.T., B.S. Jeong, A.G. Bari, et al. 2015. MapReduce based parallel gene selection method. Applied Intelligence 42 (2): 147–156.

    Article  Google Scholar 

  52. Peralta, D., S. del Río, S. Ramírez-Gallego, et al. 2015. Evolutionary feature selection for big data classification: A mapreduce approach. Mathematical Problems in Engineering 2015: 1–11.

    Article  Google Scholar 

  53. Wang, X., and O. Gotoh. 2010. A robust gene selection method for microarray-based cancer classification. Cancer Informatics 9: 15–30.

    Google Scholar 

  54. 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.

    Google Scholar 

  55. 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.

    Article  Google Scholar 

  56. 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.

    Article  Google Scholar 

  57. 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.

    Google Scholar 

  58. 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.

    Google Scholar 

  59. 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.

    Google Scholar 

  60. Dey, N., H. Das, B. Naik, & H.S. Behera (Eds.). 2019. Big data analytics for intelligent healthcare management. Academic Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, India

    Lokeswari Venkataramana, Shomona Gracia Jacob,  Saraswathi Shanmuganathan &  Venkata Vara Prasad Dattuluri

Authors
  1. Lokeswari Venkataramana
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  2. Shomona Gracia Jacob
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  3. Saraswathi Shanmuganathan
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  4. Venkata Vara Prasad Dattuluri
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Correspondence to Lokeswari Venkataramana .

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Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Minakhi Rout

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Jitendra Kumar Rout

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Himansu Das

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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

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