Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.

ABSTRACT: The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectr

SUBMITTER: Tideman LEM 

PROVIDER: S-EPMC10124144 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature