Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis.

ABSTRACT: Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDG

SUBMITTER: Beirnaert C 

PROVIDER: S-EPMC6468718 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature