Search Results

You are looking at 1 - 1 of 1 items for

  • Author: Elizabeth A Coler x
Clear All Modify Search
Elizabeth A Coler Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA

Search for other papers by Elizabeth A Coler in
Google Scholar
PubMed
Close
,
Wanxuan Chen Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA

Search for other papers by Wanxuan Chen in
Google Scholar
PubMed
Close
,
Alexey V Melnik Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
Arome Science Inc., Farmington, Connecticut, USA

Search for other papers by Alexey V Melnik in
Google Scholar
PubMed
Close
,
James T Morton Gutz Analytics LLC, Boulder, Colorado, USA

Search for other papers by James T Morton in
Google Scholar
PubMed
Close
, and
Alexander A Aksenov Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
Arome Science Inc., Farmington, Connecticut, USA

Search for other papers by Alexander A Aksenov in
Google Scholar
PubMed
Close

Artificial intelligence (AI) is rapidly revolutionizing our daily lives, as it automates mundane tasks, enhances productivity, and transforms how we interact with technology. We believe it is inevitable that AI will soon become a crucial tool in common research practices, from data analysis to writing papers. Here we explore how this transition is occurring in the field of mass spectrometry-based metabolomics, a rapidly growing area of science. Metabolomics focuses on studying small molecules in biological systems, offering valuable insights into metabolic processes and their impact on health, diseases, and physiological conditions. With the remarkable advancements in sequencing technologies and the exploration of the microbiome, the combination of sequencing and metabolomics presents profound opportunities to understand biological complexity. Incorporating AI is promising to unlock new possibilities for expanding the realms of scientific discoveries. In this review we specifically focus on the current trends in the application of AI in metabolomics research. Existing practices are examined and a perspective on future directions for integrating AI into metabolomics research is presented.

Open access