STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Genetic Modifications and Recombinations Original Research

Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review

  1. Abrar Yaqoob Department of Mathematics VIT Bhopal, Sehore
  2. Mohd Abas Bhat Department of Economics, Kashmir University, Srinagar
  3. Zeba Khan Department of biotech, vit university Bhopal, Sehore

Abstract

Dimensionality reduction techniques have become a vital tool in the investigation of high-dimensional data like gene expression profiles in cancer research. Here is a review, we deliver a comprehensive overview of dimensionality reduction techniques and their applications in cancer classification. Firstly, we introduce the concepts and approaches of dimensionality reduction, and after that, we explore several methods for decreasing dimensionality. These techniques include Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and t-Distributed Stochastic Neighbour Embedding (t-SNE). We then present a comprehensive review of applications of these techniques in cancer classification, counting lung cancer, colon cancer, breast cancer, and leukemia. Moreover, we discuss the advantages and disadvantages of different dimensionality reduction techniques in cancer classification, as well as their limitations and future directions. Finally, we summarize the most recent stage in the area and make it available for use of some recommendations for future studies. Overall, this review highlights the importance of dimensionality reduction techniques in classification of cancer and provides a valuable resource for researchers working in this field.

Keywords

References (46)

  1. Aziz R, Verma CK, Srivastava N. Artificial Neural Network Classification of High Dimensional Data with Novel Optimization Approach of Dimension Reduction. Annals of Data Science. 2018;5(4):615-635. doi:10.1007/s40745-018-0155-2
  2. Ayesha S, Hanif MK, Talib R. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion. 2020;59:44-58. doi:10.1016/j.inffus.2020.01.005
  3. L. J. P. Van Der Maaten, E. O. Postma, and H. J. Van Den Herik, “Dimensionality Reduction: A Comparative Review,” J. Mach. Learn. Res., vol. 10, pp. 1–41, 2009, doi:10.1080/13506280444000102.
  4. Adiwijaya, Wisesty UN, Lisnawati E, Aditsania A, Kusumo DS. Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification. Journal of Computer Science. 2018;14(11):1521-1530. doi:10.3844/jcssp.2018.1521.1530
  5. I. Guyon, “Gene Selection for Cancer Classification,” pp. 389–422, 2002.
  6. Lu H, Chen J, Yan K, Jin Q, Xue Y, Gao Z. A hybrid feature selection algorithm for gene expression data classification. Neurocomputing. 2017;256:56-62. doi:10.1016/j.neucom.2016.07.080
  7. Murtagh F. A Survey of Recent Advances in Hierarchical Clustering Algorithms. The Computer Journal. 1983;26(4):354-359. doi:10.1093/comjnl/26.4.354
  8. S. Shukla and S. Naganna, “A Review ON K-means DATA Clustering APPROACH,” vol. 4, no. 17, pp. 1847–1860, 2014.
  9. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M. Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis. 2020;143:106839. doi:10.1016/j.csda.2019.106839
  10. Jansi Rani M, Devaraj D. Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification. Journal of Medical Systems. 2019;43(8). doi:10.1007/s10916-019-1372-8
  11. Alomari OA, Khader AT, Betar MAA, Abualigah LM. Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. International Journal of Data Mining and Bioinformatics. 2017;19(1):32. doi:10.1504/ijdmb.2017.088538
  12. Galon J, Pagès F, Marincola FM, Angell HK, Thurin M, Lugli A, et al. Cancer classification using the Immunoscore: a worldwide task force. Journal of Translational Medicine. 2012;10(1). doi:10.1186/1479-5876-10-205
  13. Almugren N, Alshamlan H. A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification. IEEE Access. 2019;7:78533-78548. doi:10.1109/access.2019.2922987
  14. Elyasigomari V, Lee DA, Screen HRC, Shaheed MH. Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. Journal of Biomedical Informatics. 2017;67:11-20. doi:10.1016/j.jbi.2017.01.016
  15. Jain I, Jain VK, Jain R. Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification. Applied Soft Computing. 2018;62:203-215. doi:10.1016/j.asoc.2017.09.038
  16. Nguyen G, Dlugolinsky S, Bobák M, Tran V, López García Á, Heredia I, et al. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review. 2019;52(1):77-124. doi:10.1007/s10462-018-09679-z
  17. S. Cho and H. Won, “Machine Learning in DNA Microarray Analysis for Cancer Classification,” no. May 2014, 2018.
  18. Almugren N, Alshamlan H. A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification. IEEE Access. 2019;7:78533-78548. doi:10.1109/access.2019.2922987
  19. A. Yaqoob, R. M. Aziz, N. K. Verma, P. Lalwani, and A. Makrariya, “A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification,” 2023.
  20. A. Ghodsi, “Dimensionality Reduction A Short Tutorial.”
  21. Jovic A, Brkic K, Bogunovic N. A review of feature selection methods with applications. 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 2015:1200-1205. doi:10.1109/mipro.2015.7160458
  22. Asir D, Appavu S, Jebamalar E. Literature Review on Feature Selection Methods for High-Dimensional Data. International Journal of Computer Applications. 2016;136(1):9-17. doi:10.5120/ijca2016908317
  23. Data Classification. 2014. doi:10.1201/b17320
  24. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A. A review of feature selection methods on synthetic data. Knowledge and Information Systems. 2012;34(3):483-519. doi:10.1007/s10115-012-0487-8
  25. Chandrashekar G, Sahin F. A survey on feature selection methods. Computers & Electrical Engineering. 2014;40(1):16-28. doi:10.1016/j.compeleceng.2013.11.024
  26. Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Computers in Biology and Medicine. 2019;112:103375. doi:10.1016/j.compbiomed.2019.103375
  27. Ferreau HJ, Almér S, Verschueren R, Diehl M, Frick D, Domahidi A, et al. Embedded Optimization Methods for Industrial Automatic Control. IFAC-PapersOnLine. 2017;50(1):13194-13209. doi:10.1016/j.ifacol.2017.08.1946
  28. Chellappa R, Turaga P. Feature Selection. Computer Vision. 2020:1-5. doi:10.1007/978-3-030-03243-2_299-1
  29. P. Lamba and K. Rawal, “A Survey of Algorithms for Feature Extraction and Feature Classification Methods.”
  30. Roberti de Siqueira F, Robson Schwartz W, Pedrini H. Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing. 2013;120:336-345. doi:10.1016/j.neucom.2012.09.042
  31. Hadid A, Ylioinas J, Bengherabi M, Ghahramani M, Taleb-Ahmed A. Gender and texture classification: A comparative analysis using 13 variants of local binary patterns. Pattern Recognition Letters. 2015;68:231-238. doi:10.1016/j.patrec.2015.04.017
  32. Serrano Á, de Diego IM, Conde C, Cabello E. Recent advances in face biometrics with Gabor wavelets: A review. Pattern Recognition Letters. 2010;31(5):372-381. doi:10.1016/j.patrec.2009.11.002
  33. Lee SE, Min K, Suh T. Accelerating Histograms of Oriented Gradients descriptor extraction for pedestrian recognition. Computers & Electrical Engineering. 2013;39(4):1043-1048. doi:10.1016/j.compeleceng.2013.04.001
  34. Aloysius N, Geetha M. A review on deep convolutional neural networks. 2017 International Conference on Communication and Signal Processing (ICCSP). 2017:0588-0592. doi:10.1109/iccsp.2017.8286426
  35. I. Guyon, S. Gunn, and M. Nikravesh, “Feature Extraction,” 2006.
  36. Behmann J, Mahlein AK, Rumpf T, Römer C, Plümer L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture. 2014;16(3):239-260. doi:10.1007/s11119-014-9372-7
  37. K. K. Kumar, K. Chaduvula, and B. R. Markapudi, “A Detailed Survey On Feature Extraction Techniques In Image Processing For Medical Image Analysis,” vol. 07, no. 10, pp. 2275–2284, 2020.
  38. Tang KL, Li TH, Xiong WW, Chen K. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010;11(1). doi:10.1186/1471-2105-11-109
  39. Kabir MF, Chen T, Ludwig SA. A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics. 2023;3:100125. doi:10.1016/j.health.2022.100125
  40. Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L. A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics. 2017;34(4):133-144. doi:10.1016/j.tele.2017.01.007
  41. Ayyad SM, Saleh AI, Labib LM. Gene expression cancer classification using modified K-Nearest Neighbors technique. Biosystems. 2019;176:41-51. doi:10.1016/j.biosystems.2018.12.009
  42. Salem H, Attiya G, El-Fishawy N. Classification of human cancer diseases by gene expression profiles. Applied Soft Computing. 2017;50:124-134. doi:10.1016/j.asoc.2016.11.026
  43. Dashtban M, Balafar M. Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics. 2017;109(2):91-107. doi:10.1016/j.ygeno.2017.01.004
  44. Abdoh SF, Abo Rizka M, Maghraby FA. Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques. IEEE Access. 2018;6:59475-59485. doi:10.1109/access.2018.2874063
  45. Madduri A, Adusumalli SS, Sri Katragadda H, Reddy Dontireddy MK, Sarah Suhasini P. Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks. 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). 2021:755-759. doi:10.1109/spin52536.2021.9566015
  46. Yan R, Ren F, Wang Z, Wang L, Zhang T, Liu Y, et al. Breast cancer histopathological image classification using a hybrid deep neural network. Methods. 2020;173:52-60. doi:10.1016/j.ymeth.2019.06.014