Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Abstract
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment.
Keywords
Cite
@article{arxiv.1805.11178,
title = {Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles},
author = {Alexander Binder and Michael Bockmayr and Miriam Hägele and Stephan Wienert and Daniel Heim and Katharina Hellweg and Albrecht Stenzinger and Laura Parlow and Jan Budczies and Benjamin Goeppert and Denise Treue and Manato Kotani and Masaru Ishii and Manfred Dietel and Andreas Hocke and Carsten Denkert and Klaus-Robert Müller and Frederick Klauschen},
journal= {arXiv preprint arXiv:1805.11178},
year = {2018}
}