  We present the GeneScore, a concept of feature reduction for Machine Learning
analysis of biomedical data. Using expert knowledge, the GeneScore integrates
different molecular data types into a single score. We show that the GeneScore
is superior to a binary matrix in the classification of cancer entities from
SNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a
straightforward way to facilitate state-of-the-art analysis, while making use
of the available scientific knowledge on the nature of molecular data features
used.
