Liver diseases continue to represent a significant global health burden, challenging both clinicians and researchers to understand their complex pathogenesis and devise effective diagnostic and therapeutic strategies. The proliferation of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics has greatly enhanced our understanding of biological systems. Integrating these diverse data sets allows for a deeper insight into the molecular mechanisms driving liver diseases. Nevertheless, the sheer scale and complexity of omics data pose significant challenges, effectively addressed by advancements in Artificial Intelligence (AI). AI technologies, including machine learning and deep learning, are adept at managing high-dimensional data, unveiling hidden patterns and providing predictive insights. Within liver disease research, this intersection of AI and multi-omics holds the potential to reveal novel biomarkers, forecast disease progression, and tailor personalized treatment regimes. Despite its promise, the full capabilities of this synergy remain largely underutilized in tackling the intricate issues associated with liver diseases.
This Research Topic aims to provoke thought and drive research at the intersection of AI and multi-omics to improve understanding, diagnosis, and treatment of liver diseases. The high scientific significance of this endeavor includes pioneering novel integrative approaches that could also benefit other areas of disease research. Utilizing AI to decipher complex omics data promises groundbreaking discoveries in biomedicine, particularly in identifying biomarkers and drug targets that could revolutionize liver disease management and therapy. Additionally, the clinical significance of this Research Topic lies in its capacity to enhance early diagnosis and prognosis of liver diseases through AI-enabled technologies, leading to timely and more effective interventions and ultimately, superior patient outcomes.
To further explore this promising interdisciplinary field, this Research Topic encourages submissions focused on but not limited to the following areas:
• Development of AI-based algorithms to unify and interpret genomics, transcriptomics, proteomics, and metabolomics data to discover molecular mechanisms specific to liver diseases. • Identification of novel biomarkers for liver diseases using AI to analyze integrated omics datasets, enhancing early diagnosis and prediction of disease progression. • Innovation in AI-guided drug discovery processes through multi-omics data analysis, targeting novel therapeutic avenues for treating liver conditions. • Reviews on the current state of AI applications in omics data integration and their impact on clinical practices. • Perspectives and case studies on the translational application of AI and multi-omics in liver disease research, including ethical considerations and future directions.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
