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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Detalles Bibliográficos
Autores principales: Mall, Raghvendra, Cerulo, Luigi, Garofano, Luciano, Frattini, Veronique, Kunji, Khalid, Bensmail, Halima, Sabedot, Thais S, Noushmehr, Houtan, Lasorella, Anna, Iavarone, Antonio, Ceccarelli, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283452/
https://www.ncbi.nlm.nih.gov/pubmed/29361062
http://dx.doi.org/10.1093/nar/gky015

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283452/
https://www.ncbi.nlm.nih.gov/pubmed/29361062
http://dx.doi.org/10.1093/nar/gky015

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