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High dimensional model representation of log-likelihood ratio: binary classification with expression data
BACKGROUND: Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions...
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| Publicat a: | BMC Bioinformatics |
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| Autors principals: | , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
BioMed Central
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7183128/ https://ncbi.nlm.nih.gov/pubmed/32334509 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-020-3486-x |
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