Project Details
Description
Liver disease presents a significant challenge to global public health, with particularly high incidence rates in Mainland China and Hong Kong. Currently, the academic community lacks a comprehensive scientific understanding of the evolution and development of liver diseases. This gap has led to a shortage of effective diagnostic and treatment methods, hindering the efficient prevention and management of liver diseases.
To address this challenge, we are leveraging an established clinical plasma biobank encompassing various types of liver diseases. For the first time, we are employing a functional metabolomics strategy to accurately characterize the metabolic molecular profiles associated with the pathological evolution of different liver diseases. This approach allows us to construct a clinical plasma metabolite dataset for these conditions. Furthermore, by integrating artificial intelligence and deep learning analyses, we aim to precisely identify diagnostic metabolite biomarkers, enabling us to develop prognostic-predictive models for liver diseases.
Additionally, this project seeks to establish a macromolecular knowledge graph for the pathological evolution of liver diseases. This will be based on a multimodal open-source database, utilizing a combination of large language models and knowledge graph strategies. The key objectives are: 1) To accurately identify diagnostic metabolite biomarkers that can precisely phenotype the evolution and development of liver diseases, construct prognostic-predictive models, promote the development of diagnostic kits, and assist in clinical testing of various liver conditions. 2) To integrate and analyze the diagnostic biomarker dataset with the macromolecular knowledge graph, accurately identifying functional metabolites with drug-target potential for liver diseases and guiding the development of future treatment strategies.
To address this challenge, we are leveraging an established clinical plasma biobank encompassing various types of liver diseases. For the first time, we are employing a functional metabolomics strategy to accurately characterize the metabolic molecular profiles associated with the pathological evolution of different liver diseases. This approach allows us to construct a clinical plasma metabolite dataset for these conditions. Furthermore, by integrating artificial intelligence and deep learning analyses, we aim to precisely identify diagnostic metabolite biomarkers, enabling us to develop prognostic-predictive models for liver diseases.
Additionally, this project seeks to establish a macromolecular knowledge graph for the pathological evolution of liver diseases. This will be based on a multimodal open-source database, utilizing a combination of large language models and knowledge graph strategies. The key objectives are: 1) To accurately identify diagnostic metabolite biomarkers that can precisely phenotype the evolution and development of liver diseases, construct prognostic-predictive models, promote the development of diagnostic kits, and assist in clinical testing of various liver conditions. 2) To integrate and analyze the diagnostic biomarker dataset with the macromolecular knowledge graph, accurately identifying functional metabolites with drug-target potential for liver diseases and guiding the development of future treatment strategies.
| Status | Active |
|---|---|
| Effective start/end date | 1/05/25 → 30/04/28 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being
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