Feature reduction for machine learning on molecular features: The GeneScore

• URL: http://arxiv.org/abs/2101.05546v1
• Date: Thu, 14 Jan 2021 10:58:39 GMT
• Title: Feature reduction for machine learning on molecular features: The GeneScore
• Authors: Alexander Denker, Anastasia Steshina, Theresa Grooss, Frank Ueckert, Sylvia N\"urnberg
• Abstract summary: The GeneScore is a concept of feature reduction for Machine Learning analysis of biomedical data. We show that the GeneScore is superior to a binary matrix in the classification of cancer entities.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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