Transcriptomic Data

Transcriptomic data analysis focuses on understanding gene expression patterns to decipher cellular functions and disease mechanisms. Current research emphasizes developing advanced computational methods, including graph autoencoders, variational neural networks, and federated learning frameworks, to analyze high-dimensional single-cell and spatial transcriptomic data, often incorporating cell ontology information to improve biological relevance. These efforts aim to improve disease subtyping, predict drug responses, and identify novel biomarkers, ultimately advancing precision medicine and our understanding of complex biological processes. The development of robust and interpretable models is crucial for translating these analyses into clinically actionable insights.