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Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure
The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Ba...
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| I publikationen: | Data (Basel) |
|---|---|
| Huvudupphovsmän: | , , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
2017
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5358670/ https://ncbi.nlm.nih.gov/pubmed/28331847 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/data2010005 |
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