Multi Omics

Multi-omics integrates data from multiple biological sources (e.g., genomics, transcriptomics, proteomics) to gain a holistic understanding of complex biological systems, primarily aiming to improve disease diagnosis, prognosis, and treatment. Current research heavily utilizes machine learning, particularly deep learning architectures like autoencoders, graph neural networks, and transformers, along with methods such as multiple kernel learning and contrastive learning, to analyze the high-dimensional, heterogeneous data. This approach is significantly impacting fields like oncology and Alzheimer's research by enabling more accurate disease subtyping, biomarker discovery, and personalized medicine strategies through improved predictive modeling of genotype-phenotype relationships.