Cargando…

Differential Network Analysis with Multiply Imputed Lipidomic Data

The importance of lipids for cell function and health has been widely recognized, e.g., a disorder in the lipid composition of cells has been related to atherosclerosis caused cardiovascular disease (CVD). Lipidomics analyses are characterized by large yet not a huge number of mutually correlated va...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kujala, Maiju, Nevalainen, Jaakko, März, Winfried, Laaksonen, Reijo, Datta, Susmita
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2015
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378983/ https://www.ncbi.nlm.nih.gov/pubmed/25822937 http://dx.doi.org/10.1371/journal.pone.0121449

Ejemplares similares

Validation of prediction models based on lasso regression with multiply imputed data
por: Musoro, Jammbe Z, et al.
Publicado: (2014)

Addressing health disparities using multiply imputed injury surveillance data
por: Liu, Yang, et al.
Publicado: (2023)

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
por: Datta, Susmita, et al.
Publicado: (2017)

A comparison of model selection methods for prediction in the presence of multiply imputed data
por: Thao, Le Thi Phuong, et al.
Publicado: (2018)

Lipidomics: A Tool for Studies of Atherosclerosis
por: Ekroos, Kim, et al.
Publicado: (2010)

The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data
por: Wood, Angela M, et al.
Publicado: (2015)

Standardized Regression Coefficients and Newly Proposed Estimators for [Formula: see text] in Multiply Imputed Data
por: van Ginkel, Joost R.
Publicado: (2020)

SPSS Syntax for Combining Results of Principal Component Analysis of Multiply Imputed Data Sets using Generalized Procrustes Analysis
por: van Wingerde, Bart, et al.
Publicado: (2021)

A statistical framework for differential network analysis from microarray data
por: Gill, Ryan, et al.
Publicado: (2010)

EPIQ—efficient detection of SNP–SNP epistatic interactions for quantitative traits
por: Arkin, Ya’ara, et al.
Publicado: (2014)

A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods
por: Panken, A. M., et al.
Publicado: (2022)

The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?
por: Ben, Ângela Jornada, et al.
Publicado: (2022)

dna: An R package for differential network analysis
por: Gill, Ryan, et al.
Publicado: (2014)

Fish oil and krill oil differentially modify the liver and brain lipidome when fed to mice
por: Skorve, Jon, et al.
Publicado: (2015)

Missing value imputation improves clustering and interpretation of gene expression microarray data
por: Tuikkala, Johannes, et al.
Publicado: (2008)

Network multipliers and public health
por: VanderWeele, Tyler J, et al.
Publicado: (2019)

Reuse of imputed data in microarray analysis increases imputation efficiency
por: Kim, Ki-Yeol, et al.
Publicado: (2004)

Multiple imputation for analysis of incomplete data in distributed health data networks
por: Chang, Changgee, et al.
Publicado: (2020)

Estimating adjusted risk differences by multiply‐imputing missing control binary potential outcomes following propensity score‐matching
por: Austin, Peter C., et al.
Publicado: (2021)

Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models
por: Sekula, Michael, et al.
Publicado: (2022)

Exploring the importance of cancer pathways by meta-analysis of differential protein expression networks in three different cancers
por: Sikdar, Sinjini, et al.
Publicado: (2016)

A neural network analysis of Lifeways cross-generation imputed data
por: Kelly, Gabrielle E.
Publicado: (2018)

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods
por: Lehtimäki, Miikael, et al.
Publicado: (2023)

mbImpute: an accurate and robust imputation method for microbiome data
por: Jiang, Ruochen, et al.
Publicado: (2021)

The multiplier problem
por: Larsen, Ronald
Publicado: (1969)

The multiplier problem
por: Larsen, Ronald
Publicado: (1969)

The Supernova Multiplier
por: Knapp, Robert
Publicado: (2019)

ImputeGAN: Generative Adversarial Network for Multivariate Time Series Imputation
por: Qin, Rui, et al.
Publicado: (2023)

Differential projectile energy loss in multiply ionizing collisions
por: Schulz, M, et al.
Publicado: (1998)

Accuracy of Imputation for Apolipoprotein E ε Alleles in Genome-Wide Genotyping Data
por: Vuoksimaa, Eero, et al.
Publicado: (2020)

Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
por: Liao, Serena G, et al.
Publicado: (2014)

Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data
por: Stahl, Katharina, et al.
Publicado: (2021)

ImputEHR: A Visualization Tool of Imputation for the Prediction of Biomedical Data
por: Zhou, Yi-Hui, et al.
Publicado: (2021)

ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data
por: Medo, Matúš, et al.
Publicado: (2019)

DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
por: Arisdakessian, Cédric, et al.
Publicado: (2019)

Generative adversarial networks for imputing missing data for big data clinical research
por: Dong, Weinan, et al.
Publicado: (2021)

Flexible imputation of missing data
por: van Buuren, Stef
Publicado: (2018)

Missing Data and Imputation Methods
por: Schober, Patrick, et al.
Publicado: (2020)

Molecular Lipids Identify Cardiovascular Risk and Are Efficiently Lowered by Simvastatin and PCSK9 Deficiency
por: Tarasov, Kirill, et al.
Publicado: (2014)

DrImpute: imputing dropout events in single cell RNA sequencing data
por: Gong, Wuming, et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_ubtvpu43omli9cj0p6creu0c71