Feature reduction for machine learning on molecular features: The GeneScore
Genomics
2021-01-15 v1 Machine Learning
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.
Cite
@article{arxiv.2101.05546,
title = {Feature reduction for machine learning on molecular features: The GeneScore},
author = {Alexander Denker and Anastasia Steshina and Theresa Grooss and Frank Ueckert and Sylvia Nürnberg},
journal= {arXiv preprint arXiv:2101.05546},
year = {2021}
}
Comments
11 pages, 9 figures, 4 tables