Among various types of molecular data, metabolomics provides the most functional information. However, most existing technologies solely rely on mRNA, excluding metabolomics, because of the ambiguity in using large-scale metabolomics data. With other approaches that do integrate metabolomics, additional time-consuming and costly experiments are still limited to characterizing a small fraction of the metabolite masses.
We have pioneered a AI-driven technology that can measure metabolite masses fast and inexpensively. Using our proprietary database and machine-learning algorithm, we reduce the need for these additional experiments, predict the identity of each metabolite mass, and integrate these data with other large-scale molecular datasets such as genomics and proteomics.