Cargando…
HASE: Framework for efficient high-dimensional association analyses
High-throughput technology can now provide rich information on a person’s biological makeup and environmental surroundings. Important discoveries have been made by relating these data to various health outcomes in fields such as genomics, proteomics, and medical imaging. However, cross-investigation...
Autores principales: | Roshchupkin, G. V., Adams, H. H. H., Vernooij, M. W., Hofman, A., Van Duijn, C. M., Ikram, M. A., Niessen, W. J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5080584/ https://www.ncbi.nlm.nih.gov/pubmed/27782180 http://dx.doi.org/10.1038/srep36076 |
Ejemplares similares
-
GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
por: van Hilten, Arno, et al.
Publicado: (2021) -
Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings
por: Adams, Hieab H.H., et al.
Publicado: (2019) -
Aging-Dependent Genetic Effects Associated to ADHD Predict Longitudinal Changes of Ventricular Volumes in Adulthood
por: Vilor-Tejedor, Natalia, et al.
Publicado: (2020) -
Heritability of the shape of subcortical brain structures in the general population
por: Roshchupkin, Gennady V., et al.
Publicado: (2016) -
Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals
por: Phillips, Steven, et al.
Publicado: (2012)