Cancer Multi Omics Data

Cancer multi-omics data analysis integrates information from multiple biological sources (e.g., genomics, transcriptomics, proteomics) to gain a holistic understanding of cancer development and progression. Current research focuses on developing advanced machine learning models, including graph neural networks, transformers, and self-supervised learning approaches, to effectively analyze this high-dimensional data, often incorporating techniques like attention mechanisms and contrastive learning for improved performance and interpretability. These efforts aim to improve cancer subtype classification, biomarker discovery, patient stratification, and ultimately, personalized treatment strategies, thereby significantly impacting cancer research and clinical practice.