Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learn...

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Detalles Bibliográficos
Autores principales: Kegerreis, Brian, Catalina, Michelle D., Bachali, Prathyusha, Geraci, Nicholas S., Labonte, Adam C., Zeng, Chen, Stearrett, Nathaniel, Crandall, Keith A., Lipsky, Peter E., Grammer, Amrie C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610624/
https://www.ncbi.nlm.nih.gov/pubmed/31270349
http://dx.doi.org/10.1038/s41598-019-45989-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610624/
https://www.ncbi.nlm.nih.gov/pubmed/31270349
http://dx.doi.org/10.1038/s41598-019-45989-0

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