Omics and Precision Health AI

We explore how AI can be combined with multi-omics data (e.g., genomics, transcriptomics, proteomics, metabolomics) to advance precision medicine. AI algorithms can process the vast amounts of data generated by multi-omics studies to identify complex patterns, relationships, and biomarkers that might not be detectable using traditional methods. By leveraging AI, researchers can better understand the underlying mechanisms of diseases, predict individual responses to treatments, and develop personalized therapeutic strategies. This combination allows for more accurate disease diagnosis, risk prediction, and treatment customization, ultimately improving patient outcomes in complex diseases such as cancer, cardiometabolic diseases, and neurodegenerative disorders. 

View our featured work and publications

1. Yao, M., Miller, G.W., Vardarajan, B. N., Baccarelli, A. A., Guo, Z., and Liu, Z. (2024). Deciphering causal proteins in Alzheimer's disease: A novel Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction. Cell Genomics (Accepted). 
2. Chuwdhury, G.,  Guo, Y.*,  Cheung, C.,   Lam, K.,  Kam, N.,  Liu, Z.#,  Dai, W.#, (2024) ImmuneMirror: A Machine Learning-based Integrative Pipeline and Web Server for Neoantigen Prediction. Briefings in Bioinformatics, 25(2), bbae024.