AI in multi-omics analysis of liver diseases.

Source: PubMed, NCBI / U.S. National Library of Medicine

Liver diseases, including hepatocellular carcinoma (HCC), Cholangiocarcinoma (CCA), non-alcoholic fatty liver disease (NAFLD), and cirrhosis, account for over 2 million deaths each year worldwide. Due to their intricate etiology, which encompasses genetic, epigenetic, environmental, and metabolic factors cause late diagnosis. Advances in multi-omics techniques generate huge and complex data including genomics, epigenomics, transcriptomics, proteomics, and metabolomics which revolutionize the understanding of biological systems at different layers of complexity. Furthermore, advances in integrating multi-omics data using artificial intelligence(AI) helping in identifying common factors dysregulated at different layers in biological systems to identify the disease etiology, diseases subtyping, diagnosis and prognosis modeling. This chapter discusses the common omics data and application of AI in the integration of multi-omics data for deep investigation of liver diseases to enhance the understanding of disease mechanisms, identify biomarkers, and discover therapeutic targets for the progression of precision medicine. Furthermore, we discuss about persistent challenges in integrating heterogeneous omics datasets including variations in data format, scale, and AI model interpretability. The incorporation of AI-driven multi-omics approach in clinical hepatology will support more accurate and early diagnosis, disease subtyping, and better treatment planning for precision medicine.