Results 71 to 80 of about 204,036 (213)

Improved Imputation of Common and Uncommon Single Nucleotide Polymorphisms (SNPs) with a New Reference Set

, 2011
Statistical imputation of genotype data is an important technique for analysis of genome-wide association studies (GWAS). We have built a reference dataset to improve imputation accuracy for studies of individuals of primarily European descent using ...
Susan M. Gabstur, Jennifer Stone, Zhaoming Wang, Charles C. Chung, Phil Taylor, Demetrius Albanes, Ann W. Hsing, Xiang Deng, Michael A. Eberle, Daniel Stram, Amy Hutchinson, Brian E. Henderson, Margaret Tucker, Christopher Haiman, Jarmo Virtamo, Sonja I. Berndt, Brian Henderson, Mark Purdue, Susan Gabstur, Joshua Sampson, Sonja Berndt, Michael Eberle, W. Ryan Diver, Stephen Chanock, Ann Hsing, Mark P. Purdue, Lauren Teras, Kevin Jacobs, Nilanjan Chatterjee, Jennifer L. Stone, Christopher M. Haiman, Charles Chung, Meredith Yeager, Amy Hutchinson, Lauren R. Teras   +34 more
core   +1 more source

SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data

Communications Biology
ABSTRACT Chromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair ...
Hongyu Yu, Daiqing Wu, Guning Shen, Ming Hu, Yun Li   +4 more
openaire   +5 more sources

Advanced imputation techniques in failure prediction with optimized machine learning models using genetic algorithms

Discover Computing
This study highlights the impact of missing data imputation techniques in failure prediction. Existing studies have focused less on the issue of missing data, examined less the overall performance of the imputation techniques, and often concentrated on ...
Kaoutar El Madou, Moad El Kharrim, Mohamed El Merouani   +2 more
doaj   +1 more source

Imputation of Missing Data in Waves 1 and 2 of SHARE [PDF]

The Survey of Health, Ageing and Retirement in Europe (SHARE), like all large household surveys, suffers from the problem of item non-response, and hence the need of imputation of missing values arises. In this paper I describe the imputation methodology
Dimitrios Christelis
core  

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study [PDF]

, 2010
Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the ...
Holder, Roger, Douglas G Altman, Holder, RL, Royston, Patrick, Royston, P, Marshall, A, Patrick Royston, Holder, Roger L., Altman, DG, Roger L Holder, Altman, Douglas G., Andrea Marshall, Marshall, A. (Andrea)   +12 more
core   +1 more source

A Comparative Study on Missing Value Imputation Techniques in Machine Learning [PDF]

SHS Web of Conferences
Handling missing values is a crucial step in data preprocessing, as incomplete data can significantly impact model performance and overall data integrity.
Meng Haoyu
doaj   +1 more source

Multiple imputation for unit-nonresponse versus weighting including a comparison with a nonresponse follow-up study [PDF]

The results of a national fear of crime survey are compared with results following the use of different nonresponse correction procedures. We compared naive estimates, weighted estimates, estimates after a thorough nonresponse follow-up and estimates ...
Rässler, Susanne, Schnell, Rainer
core  

Propensity score matching with missing covariates via iterated, sequential multiple imputation

, 2011
In many observational studies, analysts estimate causal effects using propensity score matching. Estimation of propensity scores is complicated when covariate values intended for collection are in fact missing. To handle the missing data, one approach is
Reiter, Jerome P., Mitra, Robin
core  

An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder

IEEE Access, 2018
This paper proposes an imputation method for missing data based on an extreme learning machine auto-encoder (ELM-AE). The imputation chooses a set of plausible values determined by ELM-AE and then substitutes the average of these plausible values for the
Cheng-Bo Lu, Ying Mei
doaj   +1 more source

Implementation, evaluation and application of multiple imputation for missing data in longitudinal electronic health record research

, 2015
Longitudinal electronic health records are a valuable resource for research because they contain information on many patients over long follow-up periods.
Welch, CA
core