AI/ML Now: Can Bayesian Graph Theory Work in the Routine Laboratory?

In this LCGC International video for the AI/ML Now interview series, Gabriel Vivó Truyols from the Spanish Council for Scientific Research, Madrid, Spain. discusses his work on using graph theory and Bayesian statistics for peak assignment and chromatographic alignment.¹ Matching peaks across chromatograms is a key step in data processing, but traditional alignment and peak-matching methods are deterministic and commit to a single answer, even when several assignments are plausible. Vivó-Truyols explains why he brought peak assignment and alignment together in one graph-theory framework, and how a Bayesian treatment of multiple possible assignments can make the results more robust. He also talks about the main bottlenecks, and how practical it would be to bring the approach into commercial chromatography software for semi-automated data curation in routine labs.

In the interview, he answered the following question:
• How feasible is it to integrate this Bayesian graph-theory methodology into existing commercial chromatography software workflows, particularly for semi-automated data curation in routine analytical laboratories?

Watch the short video interview here:

View the complete interview below:

AI/ML Now: Using Graph Theory and Bayesian Statistics for Peak Assignment and Chromatographic Alignment

AI/ML Now: Peak Assignment and Alignment: Practical Aspects of a Graph Theory and a Bayesian Statistics Approach

AI/ML Now: Can Bayesian Graph Theory Work in the Routine Laboratory?

Reference

1. On the Use of Graph Theory for Peak Assignation and Chromatographic Alignment: A Bayesian Approach. Presentation at HTC-19, Leuven, Belgium.