Cargando…
NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses
Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of ef...
Autores principales: | Wang, Shisheng, Li, Wenxue, Hu, Liqiang, Cheng, Jingqiu, Yang, Hao, Liu, Yansheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641313/ https://www.ncbi.nlm.nih.gov/pubmed/32526036 http://dx.doi.org/10.1093/nar/gkaa498 |
Ejemplares similares
-
Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics
por: Gardner, Miranda L., et al.
Publicado: (2021) -
Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer
por: Li, Xia, et al.
Publicado: (2011) -
Accounting for missing data in statistical analyses: multiple imputation is not always the answer
por: Hughes, Rachael A, et al.
Publicado: (2019) -
Optimization methods for the imputation of missing values in Educational Institutions Data
por: Aureli, D., et al.
Publicado: (2021) -
Missing value imputation using least squares techniques in contaminated matrices
por: Garcia-Peña, Marisol, et al.
Publicado: (2022)