Feature reduction for machine learning on molecular features: The GeneScore

Alexander Denker; Anastasia Steshina; Theresa Grooss; Frank Ueckert; Sylvia Nürnberg

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arXiv · 2101.05546

Feature reduction for machine learning on molecular features: The GeneScore

Alexander Denker·Anastasia Steshina·Theresa Grooss·Frank Ueckert·Sylvia Nürnberg

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Abstract

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.

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Alexander Denker, Anastasia Steshina, Theresa Grooss, Frank Ueckert, Sylvia Nürnberg. 2021-01-14. Feature reduction for machine learning on molecular features: The GeneScore. https://arxiv.org/abs/2101.05546

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