Cargando…
Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features
Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordinati...
Autores principales: | Bartlett, Christopher W., Klamer, Brett G., Buyske, Steven, Petrill, Stephen A., Ray, William C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771148/ https://www.ncbi.nlm.nih.gov/pubmed/31546899 http://dx.doi.org/10.3390/genes10090727 |
Ejemplares similares
-
The Rosetta Phenotype Harmonization Method Facilitates Finding a Relationship Quantitative Trait Locus for a Complex Cognitive Trait
por: Petrill, Stephen A., et al.
Publicado: (2023) -
Metabolomic Profiling of Malaysian and New Zealand Honey Using Concatenated NMR and HRMS Datasets
por: Yusoff, Yusnaini M., et al.
Publicado: (2022) -
Gaze Tracking Based on Concatenating Spatial-Temporal Features
por: Hwang, Bor-Jiunn, et al.
Publicado: (2022) -
Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?
por: Asselineau, J., et al.
Publicado: (2018) -
Regression on imperfect class labels derived by unsupervised clustering
por: Brøndum, Rasmus Froberg, et al.
Publicado: (2020)