Missing value imputation for microarray data: a comprehensive comparison study and a web tool

Missing value imputation for microarray data: a comprehensive comparison study and a web tool

Bibliographic Details
Main Authors:	Chiu, Chia-Chun, Chan, Shih-Yao, Wang, Chung-Ching, Wu, Wei-Sheng
Format:	Online
Language:	English
Published:	BioMed Central 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4028811/

Similar Items

Shrinkage regression-based methods for microarray missing value imputation
by: Wang, Hsiuying, et al.
Published: (2013)

Robust imputation method for missing values in microarray data
by: Yoon, Dankyu, et al.
Published: (2007)

Improving missing value imputation of microarray data by using spot quality weights
by: Johansson, Peter, et al.
Published: (2006)

A hybrid imputation approach for microarray missing value estimation
by: Li, Huihui, et al.
Published: (2015)

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

A comparison of various imputation methods for missing values in air quality data
by: Nuryazmin Ahmat Zainuri,, et al.
Published: (2015)

A Comparison of Various Imputation Methods for Missing Values in Air Quality Data
by: Muhammad Nor Hisyam Bin Kamarudin
Published: (2015)

Imputing missing values in modelling the PM10 concentrations
by: Nuradhiathy Abd Razak,, et al.
Published: (2014)

Missing values in data analysis: Ignore or impute?
by: Husna Zayadi
Published: (2011)

Imputing missing values in modelling the PM10 concentrations
by: Lydia Iryani Binti Mohamed Khairul Banks
Published: (2014)

MISSING VALUE IMPUTATION AND NORMALIZATION TECHNIQUES IN MYOCARDIAL INFARCTION
by: K Manimekalai, et al.
Published: (2018-04-01)

Comparison of imputation methods for missing laboratory data in medicine
by: Waljee, Akbar K, et al.
Published: (2013)

Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme
by: Wang, Xian, et al.
Published: (2006)

Impact of Missing Value Imputation on Classification for DNA Microarray Gene Expression Data—A Model-Based Study
by: Sun, Youting, et al.
Published: (2009)

An Imputation Approach for Oligonucleotide Microarrays
by: Li, Ming, et al.
Published: (2013)

Missing Data: data replacement and imputation
by: Graeme, Hutcheson
Published: (2012)

Microarray missing data imputation based on a set theoretic framework and biological knowledge
by: Gan, Xiangchao, et al.
Published: (2006)

Analyzing data with missing continuous covariates by multiple imputation using proper imputation
by: Husna Zayadi
Published: (2011)

Performance analysis of machine learning algorithms for missing value imputation
by: Zainal Abidin, Nadzurah, et al.
Published: (2018)

Imputation of missing values of tumour stage in population-based cancer registration
by: Eisemann, Nora, et al.
Published: (2011)

Normalization and missing value imputation for label-free LC-MS analysis
by: Karpievitch, Yuliya V, et al.
Published: (2012)

Analysis of the progression of systolic blood pressure using imputation of missing phenotype values
by: Vaitsiakhovich, Tatsiana, et al.
Published: (2014)

Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data
by: Wu, Chong, et al.
Published: (2016)

Image microarrays (IMA): Digital pathology's missing tool
by: Hipp, Jason, et al.
Published: (2011)

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

Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
by: Shah, Anoop D., et al.
Published: (2014)

A nonparametric multiple imputation approach for missing categorical data
by: Muhan Zhou, et al.
Published: (2017-06-01)

Imputing missing genotypes: effects of methods and patterns of missing data
by: Ogut, Funda, et al.
Published: (2011)

Integrative missing value estimation for microarray data
by: Hu, Jianjun, et al.
Published: (2006)

Missing variability in meta-analysis : is imputing always good?
by: Nik Idris, Nik Ruzni, et al.
Published: (2006)

Imputation methods for missing data for polygenic models
by: Fridley, Brooke, et al.
Published: (2003)

Fast accurate missing SNP genotype local imputation
by: Wang, Yining, et al.
Published: (2012)

Multiple Imputation of Missing Composite Outcomes in Longitudinal Data
by: O’Keeffe, Aidan G., et al.
Published: (2016)

On imputation for planned missing data in context questionnaires using plausible values: a comparison of three designs
by: David Kaplan, et al.
Published: (2018-06-01)

Comparison of imputation variance estimators
by: Hughes, RA, et al.
Published: (2014)

Evaluating the Coverage and Potential of Imputing the Exome Microarray with Next-Generation Imputation Using the 1000 Genomes Project
by: Tantoso, Erwin, et al.
Published: (2014)

Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering
by: de Brevern, Alexandre G, et al.
Published: (2004)

Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
by: Roslinazairimah, Zakaria, et al.
Published: (2015)

Determination of the best single imputation algorithm for missing rainfall data treatment
by: Gamil Abdulraqeb Abdullah Saeed,, et al.
Published: (2016)

Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
by: Saeed, Gamil Abdulraqeb Abdullah, et al.
Published: (2016)