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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