Results 41 to 50 of about 202,004 (267)

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Frontiers in Genetics, 2022
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction.
Tianyu Deng, Pengfei Zhang, Dorian Garrick, Huijiang Gao, Lixian Wang, Fuping Zhao   +5 more
doaj   +1 more source

Spatial and single‐nuclei transcriptomics reveals idiosyncratic and generic patterns in papillary and anaplastic thyroid cancers

Molecular Oncology, EarlyView.
Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur, Joel Rodrigues Vitória, Manuel Saiselet, Ligia Craciun, Denis Larsimont, Anne Lefort, Frédérick Libert, Carine Maenhaut, Sabine Costagliola, Maxime Tarabichi, Mirian Romitti, Vincent Detours   +11 more
wiley   +1 more source

Imputation Using Training Labels and Classification via Label Imputation

IEEE Access
Missing data is a common problem in practice, and various imputation methods have been developed to deal with missing data. However, even though in many cases, the labels are available in the training data, the common practice of imputation usually only ...
Thu Nguyen, Uyen Dang, Tuan L. Vo, Pal Halvorsen, Michael A. Riegler   +4 more
doaj   +1 more source

Evaluating Performance of Missing Data Imputation Methods in IRT Analyses

International Journal of Assessment Tools in Education, 2018
Missing data is a common problem in datasets thatare obtained by administration of educational and psychological tests. It is widelyknown that existence of missing observations in data can lead to serious problemssuch as biased parameter estimates and ...
Ömür Kaya Kalkan, Yusuf Kara, Hülya Kelecioğlu   +2 more
doaj   +1 more source

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

PLoS ONE, 2020
BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll, J Turicchi, C Duarte, J Michalowska, S C Larsen, A L Palmeira, B L Heitmann, G W Horgan, R J Stubbs   +8 more
doaj   +1 more source

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]

PLoS ONE, 2015
Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi, Tandi E Matsha, Rajiv T Erasmus, Andre P Kengne   +3 more
doaj   +1 more source

White Matter Hyperintensity Burden and Short‐Interval Change Associated With Sleep Apnoea in the UK Biobank

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We
Peng Cheng, Diego Z. Carvalho, Shahid Karim, Anwar Chahal, Virend K. Somers   +4 more
wiley   +1 more source

MIAEC: Missing Data Imputation Based on the Evidence Chain

IEEE Access, 2018
Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu, Weizhi Chong, Shancang Li, Abdullahi Arabo, Jianyu Xiao   +4 more
doaj   +1 more source

Practical strategies for handling breakdown of multiple imputation procedures

Emerging Themes in Epidemiology, 2021
Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the ...
Cattram D. Nguyen, John B. Carlin, Katherine J. Lee   +2 more
doaj   +1 more source

Generative Adversarial Networks Assist Missing Data Imputation: A Comprehensive Survey and Evaluation

IEEE Access, 2023
Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the ...
Reza Shahbazian, Sergio Greco
doaj   +1 more source