Optimizing Deep Learning with Dimensionality Reduction for Analyzing the CuMiDa Brain Cancer Gene Expression Dataset

In the digital era, machine learning and deep learning have become indispensable tools for bioinformatics, particularly in analyzing high-dimensional gene expression data for cancer diagnosis and classification. This study leverages the CuMiDa brain cancer dataset, a curated microarray database with 54,676 genes and 130 samples, to evaluate the effectiveness of deep learning models integrated with dimensionality reduction techniques. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (TruncatedSVD) were employed to address the challenges of high-dimensional data, reducing noise and computational complexity. Three deep learning models—DNN, MLP, and TabNet—were implemented with various optimizers, including ADAM, RMSprop, and SGD. Results showed that TruncatedSVD outperformed PCA in minimizing loss, especially for MLP with LBFGS optimizers, achieving near-zero loss. TabNet demonstrated the highest classification accuracy (96%) with ADAM and RMSprop. Conversely, SGD exhibited suboptimal performance across models. These findings highlight the critical role of dimensionality reduction and optimizer selection in enhancing the efficiency and accuracy of deep learning models for cancer classification. This research provides a robust framework for improving diagnostic tools in computational oncology.