Results 41 to 50 of about 230,992 (300)

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

Applied Sciences, 2022
The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation.
Luis Alfonso Menéndez García, Marta Menéndez Fernández, Violetta Sokoła-Szewioła, Laura Álvarez de Prado, Almudena Ortiz Marqués, David Fernández López, Antonio Bernardo Sánchez   +6 more
doaj   +1 more source

Efficient phasing and imputation of low-coverage sequencing data using large reference panels

Nature Genetics, 2020
Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies.
S. Rubinacci, Diogo M. Ribeiro, R. Hofmeister, O. Delaneau   +3 more
semanticscholar   +1 more source

Sequencing genes in silico using single nucleotide polymorphisms

BMC Genetics, 2012
Background The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects.
Zhang Xinyi, Zhang Bo, Li Shuying, Huang Xin, Hansen John A, Zhao Lue   +5 more
doaj   +1 more source

PhosR enables processing and functional analysis of phosphoproteomic data

Cell Reports, 2021
Summary: Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale.
Hani Jieun Kim, Taiyun Kim, Nolan J. Hoffman, Di Xiao, David E. James, Sean J. Humphrey, Pengyi Yang   +6 more
doaj   +1 more source

Missing Data in Clinical Research: A Tutorial on Multiple Imputation

Canadian Journal of Cardiology, 2020
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the variables of interest are not measured or recorded for all subjects in the sample.
P. Austin, I. White, Douglas S. Lee, S. van Buuren   +3 more
semanticscholar   +1 more source

The Challenges of Chromosome Y Analysis and the Implications for Chronic Kidney Disease

Frontiers in Genetics, 2019
The role of chromosome Y in chronic kidney disease (CKD) remains unknown, as chromosome Y is typically excluded from genetic analysis in CKD. The complex, sex-specific presentation of CKD could be influenced by chromosome Y genetic variation, but there ...
Kerry Anderson, Marisa Cañadas-Garre, Robyn Chambers, Alexander Peter Maxwell, Alexander Peter Maxwell, Amy Jayne McKnight   +5 more
doaj   +1 more source

Multiple Imputation After 18+ Years

, 1996
Multiple imputation was designed to handle the problem of missing data in public-use data bases where the data-base constructor and the ultimate user are distinct entities. The objective is valid frequency inference for ultimate users who in general have
D. Rubin
semanticscholar   +1 more source

Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method

Cell Genomics, 2022
Summary: The killer cell immunoglobulin-like receptor (KIR) recognizes human leukocyte antigen (HLA) class I molecules and modulates the function of natural killer cells.
Saori Sakaue, Kazuyoshi Hosomichi, Jun Hirata, Hirofumi Nakaoka, Keiko Yamazaki, Makoto Yawata, Nobuyo Yawata, Tatsuhiko Naito, Junji Umeno, Takaaki Kawaguchi, Toshiyuki Matsui, Satoshi Motoya, Yasuo Suzuki, Hidetoshi Inoko, Atsushi Tajima, Takayuki Morisaki, Koichi Matsuda, Yoichiro Kamatani, Kazuhiko Yamamoto, Ituro Inoue, Yukinori Okada   +20 more
doaj   +1 more source

When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

BMC Medical Research Methodology, 2017
BackgroundMissing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the ...
J. Jakobsen, C. Gluud, J. Wetterslev, P. Winkel   +3 more
semanticscholar   +1 more source

Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study

Statistical Methods in Medical Research, 2023
Background: In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome ...
R. Sisk, M. Sperrin, Niels Peek, M. van Smeden, G. Martin   +4 more
semanticscholar   +1 more source