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RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes
We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes...
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| Publicado no: | Nucleic Acids Res |
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| Main Authors: | , , , , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Oxford University Press
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6283452/ https://ncbi.nlm.nih.gov/pubmed/29361062 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/nar/gky015 |
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