Open, integrative, and extendable, artificial intelligence and knowledge graphs framework for functional and actionable metabolomics – MetaboLinkAI

MetaboLinkAI aspires to revolutionize the analysis and interpretation of metabolomics data through a multidisciplinary approach that combines a comprehensive knowledge graph hub (MetaKH) with cutting-edge artificial intelligence (AI) and machine learning (ML) techniques. The project's main goals are to enhance the querying and ease of use of metabolomics data, improve research efficiency, and stimulate creativity in the field. These objectives are set to surpass current standards by creating an encyclopedic and expandable knowledge base, integrating advanced AI to handle the uncertainties of experimental data, and enabling a broader range of hypothesis testing and evaluation. The research approach is structured around three interconnected pillars:
WP1 - Metabolomics Use Cases: To ensure that the project tackles relevant use cases, we chose two significant and challenging areas in which metabolomics is key: the analysis of metabolites in a biomedical context, i.e. to infer metabolic activity and regulation, and the analysis of metabolite in natural product research, i.e. for characterization of chemodiversity and bioactive scaffold discovery. These use cases are designed to guide the project's development and provide benchmarks for its progress.
WP2 - Knowledge Representation and Management: The creation of an open knowledge hub, MetaKH, is central to the project. It involves (i) aggregating existing resources on chemicals, ontologies, reactions and pathways, bioactivity, publications, etc., (ii) enriching this foundational knowledge graph with metabolomics data, and (iii) establishing federated querying capabilities over the integrated knowledge hub.
WP3 - AI Research Assistant and Graph Machine Learning: This pillar focuses on developing innovative methodologies and tools, such as natural language processing and graph mining methods, to enhance data interaction, analysis capabilities, and representation of uncertainty. An AI research assistant will facilitate direct interaction with the data and knowledge through querying and summarizing, while integrated graph mining methods will address the ontological characteristics of metabolomics data.
MetaboLinkAI aims to create significant shifts in metabolomics research by democratizing data mining, broadening hypothesis evaluation, and transforming education in the field. The integration of AI technologies aims to address data uncertainties, enhance explainability, and facilitate complex data analysis. Looking ahead, the project envisages expanding its infrastructure to incorporate other omics data, laying the groundwork for a holistic, AI-augmented discovery platform in life sciences research.