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.