An Elastic Net Approach to Logistic Regression for Genetic Selection in High-Dimensional Brain Cancer Data
DOI:
https://doi.org/10.24086/cuesj.v9n1y2025.pp14-23Keywords:
Brian Cancer, Elastic net, Regularization Techniques, Gene Selection, Multinomial Logistic Model, High-Dimensional DataAbstract
The study explores issues related to the treatment of brain cancer caused by the heterogeneous nature of different variants of brain tumors. The objective of this study was to identify essential genes present in multiple types of brain cancer by using high dimensional gene expression data available on the Curated Microarray Database CuMiDa. The study’s dataset comprised a total of 130 samples belonging to 4 subtypes of brain cancer and 16384 gene expression variables. Thus, the penalized Elastic net method in conjunction with Multinomial Logistic Regression was used to cope with curse of dimensionality problems. Then, accuracy, Kappa statistic, Area Under the Curve, and F1-score were utilized to evaluate measures of the model efficiency. Elastic Net proved to be quite effective in the sense of the extensiveness of the variables included in the analysis and successfully restricted gene level further analysis as well as highlighted subtype specific expression signatures. The model achieved high precision and AUC values indicating that in general the model had good ability to distinguish all subtypes with some around perfect score of AUC. Robust parameter estimation was supplemented with cross validation and other predictive model validation statistical techniques done in R language programming. Thus, these findings suggest that the best model for evaluating large-scale gene expression data of brain cancers is the use of MLR with an elastic net regularization. There is ample evidence that these selected genes contribute to and serve as targets for therapy, therefore making this study a good starting point for further investigations with respect to understanding their biological role. The corresponding model is also to be applied to test its validity on some other datasets of a quite different nature. This, in turn, may suggest improved diagnostic, prognostic and therapeutic options for the brain tumor.
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Accepted 2024-12-20
Published 2025-01-20
