Omics Data

Omics data analysis aims to extract meaningful biological insights from high-dimensional datasets encompassing genomics, transcriptomics, metabolomics, and other molecular profiles. Current research heavily utilizes graph neural networks, including Graph Convolutional Networks and more advanced architectures like PathFormer and graph transformers, to model complex relationships within and between these data types, improving disease diagnosis accuracy and biomarker identification. These advancements, coupled with the development of user-friendly software platforms like GenoCraft and AutoBA, are democratizing access to sophisticated omics analysis, accelerating biomedical research and potentially leading to more precise and personalized medicine.