期刊
BIOINFORMATICS
卷 36, 期 15, 页码 4301-4308出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa483
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资金
- German Federal Ministry of Education and Research (BMBF) [01ZX1510, 01ZX1708E]
Motivation: High-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets. Results: The self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of interest. The hybrid approach synthetic features is not recommended because of its high false discovery rate.
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