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Recent advances in cancer research largely rely on new developments inmicroscopic or molecular profiling techniques offering high level of detailwith respect to either spatial or molecular features, but usually not both.Here, we present a novel machine learning-based computational approach thatallows for the identification of morphological tissue features and theprediction of molecular properties from breast cancer imaging data. Thisintegration of microanatomic information of tumors with complex molecularprofiling data, including protein or gene expression, copy number variation,gene methylation and somatic mutations, provides a novel means tocomputationally score molecular markers with respect to their relevance tocancer and their spatial associations within the tumor microenvironment.

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles