Machine Learning & Artificial Intelligence

We have spent the last 20 years building the best metabolomic competency in the world. Today, we enable, accelerate, and support drug development through biomarker discovery, understanding mechanisms of action, patient stratification, and more.

Metabolon measures biochemical changes through the study of small molecules to provide a profound understanding of biological systems. Data science enables the continuous improvement of our industry-leading capabilities, allowing us to provide a broader view beyond any other metabolomic provider.

As part of our data science initiatives, we leverage artificial intelligence and machine learning technologies to extract insight and recognize data patterns computationally. Metabolon’s machine learning capability improves our data curation throughput, identifies biochemical signatures, and detects anomalies to ensure accurate and rapid quality control. Our data science and machine learning capabilities provide improved coverage, quality, turnaround time, interpretations, and, ultimately, study success for our clients.

Machine learning’s effectiveness is strongly correlated to the accuracy, breadth, and consistency of the data on which it is trained. Long before the rise of data science, Metabolon professionals were generating expertly curated data sets, precise statistical analyses, and reports containing deep biological insights.

Machine learning allows us to achieve this same high-quality data, but much faster. We can bring a data set directly to quality control through machine learning, saving time by performing initial curation automatically. Machine learning also allows us to quickly determine with certainty which compounds are present and the ones that were never present, significantly reducing or even eliminating the need for human experts to make these decisions.

Built on historical data, machine learning feeds an auto curation utility that can curate many routine compounds. Consider cholesterol, which is readily found in human plasma and, therefore, not an efficient use of staff expertise to positively identify this compound. With the support of machine learning tools, we can leverage our human expert curators’ skills in delving for the presence of ethylparaben sulfate, which often presents with interfering ions, or differentiating between compounds like isoleucylglycine and alanylvaline. These compounds are not chromatographically separated but have distinct MS/MS fragmentation, usually containing a tremendous variety of information.