Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development

Dear Colleagues,

Altered metabolism has been linked to nearly every disease, including cancer, neurodegeneration, cardiovascular disease, transplantation, aging and many more. Not unexpectedly, altered metabolites already provide powerful clinical biomarkers to diagnose diseases and guide treatments. However, due to challenges in analytical measurements, most clinical assays to date only measure a limited number of metabolites, leaving the true potential largely untapped.

With advancements in technology in NMR and MS, coupled with machine learning and a deeper understanding of biology, full metabolomics studies are on the horizon. The ability to measure thousands of metabolites in a single sample is now feasible. This should usher in a new era of clinical insights driving diagnostic innovation and accelerating drug development. However, reproducibility concerns, sample logistics, metabolite annotations and questions around complex statistics must be addressed. In this Special Issue, we highlight technical advancements (and lingering limitations) driving the field. Furthermore, we provide use cases in which metabolomics and machine learning are changing our ability to diagnose and treat disease. Finally, we provide tangible best practices and considerations for those looking to apply metabolomics and ML to their research.      

Dr. Elizabeth O’Day
Guest Editor

Manuscript Submission Information

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• metabolomics
• machine learning
• biomarkers
• diagnostics
• drug development
• NMR
• MS
• precision medicine
• personalized medicine
• omics