Utilizing Neurons to Interrogate Cancer: Integrative Analysis of Cancer Omics Data With Deep Learning Models

Genomics plays an essential role in the early detection, classification, and targeted cancer therapy based on the analysis of precise alterations at the molecular level. Using the most reliable approach is essential for the exact interrogation and cross-examination of complex and multi-high-dimensional "Multi-omics" cancer genomics data. In recent years, deep learning has been successfully utilized to deal with large cancer genomics data and has the potential to transform predictive biology. This review aims to explore the recent advancements in the application of deep learning models in basic cancer omics research, including different methodologies for the interrogation of bulk cancer omics data and the importance of cross-platform data integration. The paper provides insights into advantages, limitations, potential for improvement, research gaps, future direction, and an in-depth comparison of the models currently used in the field of cancer genomics, highlighting the crucial need for collaboration and interdisciplinary research in the field.