Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review - Abrar Yaqoob; Mohd Abas Bhat; Zeba Khan (International Journal of Genetic Modifications and Recombinations, 2023)
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: Feature Extraction; cancer classification; dimensionality reduction; feature selection