Multi-Omics and Artificial Intelligence-Guided Data Integration in Chronic Liver Disease: Prospects and Challenges for Precision Medicine
Biaoyang Lin, Yingying Ma, Shengjun Wu
Abstract
Chronic liver disease (CLD) is a significant planetary health burden. CLD includes a broad range of liver pathologies from different causes, for example, hepatitis B virus infection, fatty liver disease, hepatocellular carcinoma, and nonalcoholic fatty liver disease or the metabolic associated fatty liver disease. Biomarker and diagnostic discovery, and new molecular targets for precision treatments are timely and sorely needed in CLD. In this context, multi-omics data integration is increasingly being facilitated by artificial intelligence (AI) and attendant digital transformation of systems science. While the digital transformation of multi-omics integrative analyses is still in its infancy, there are noteworthy prospects, hope, and challenges for diagnostic and therapeutic innovation in CLD. This expert review aims at the emerging knowledge frontiers as well as gaps in multi-omics data integration at bulk tissue levels, and those including single cell-level data, gut microbiome data, and finally, those incorporating tissue-specific information. We refer to AI and related digital transformation of the CLD research and development field whenever possible. This review of the emerging frontiers at the intersection of systems science and digital transformation informs future roadmaps to bridge digital technology discovery and clinical omics applications to benefit planetary health and patients with CLD.