Revealing the Spatial-Molecular Fingerprint of BMP4 Expression in Breast Cancer Using ToF-SIMS and Multiple Instance Learning

Abstract

Hyperspectral materials characterization techniques are becoming ubiquitous and technologically more sophisticated. There has been widespread development and application of machine/statistical learning to better understand large hyperspectral data sets. This has been remarkably fruitful across domains, yet many challenges remain. One important challenge is the utilization of large-scale data sets to uncover generalizable, population-level spatial-molecular differences between materials. We describe the application of mass spectrometry imaging and a novel weakly supervised learning method to identify the spatial-molecular signatures that differentiate two highly similar sets of triple-negativ..