Results 31 to 40 of about 178,240 (253)
Advanced methods for missing values imputation based on similarity learning [PDF]
PeerJ Computer Science, 2021 The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values.
Khaled M. Fouad +3 more
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SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data
Communications BiologyABSTRACT 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 +4 more
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Investigating Some Imputation Methods of Multivariate Imputation Chained Equations
European Journal of Mathematics and Statistics, 2022 This paper investigates three MICE methods: Predictive Mean Matching (PMM), Quantile Regression-based Multiple Imputation (QR-basedMI) and Simple Random Sampling Imputation (SRSI) at imputation numbers 5, 15, 20 and 30 with 5% and 20% missing values, to ascertain the one that produces imputed values that best matches the observed values and compare the
M. T. Nwakuya, E. O. Biu
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Applied Medical Informatics, 2021 Background: Not all datasets are created equal. There are some happy scenarios when the researcher has the luxury of curating the dataset and ensuring all the desired fields are filled.
Marius FERSIGAN, Marius MĂRUȘTERI
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An efficient ensemble method for missing value imputation in microarray gene expression data
BMC Bioinformatics, 2021 Background The genomics data analysis has been widely used to study disease genes and drug targets. However, the existence of missing values in genomics datasets poses a significant problem, which severely hinders the use of genomics data.
Xinshan Zhu +5 more
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Imputation of ungenotyped individuals based on genotyped relatives using Machine Learning Methodology [PDF]
Journal of Epigenetics, 2021 Machine learning methods have been used in genetic studies to build models capable of predicting missing genotypes for both human and animal genetic variations. Genotype imputation is an important process of predicting unknown genotypes. The objective of
Naeem Rastin Bojnord +3 more
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Imputation methods for filling missing data in urban air pollution data for Malaysia [PDF]
Urbanism. Arhitectura. Constructii, 2018 The air quality measurement data obtained from the continuous ambient air quality monitoring (CAAQM) station usually contained missing data. The missing observations of the data usually occurred due to machine failure, routine maintenance and human error.
Nur Afiqah Zakaria +1 more
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Sensors, 2020 Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important.
Liang Zhang
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Multiple Imputation in a Longitudinal Cohort Study: A Case Study of Sensitivity to Imputation Methods [PDF]
American Journal of Epidemiology, 2014 Multiple imputation has entered mainstream practice for the analysis of incomplete data. We have used it extensively in a large Australian longitudinal cohort study, the Victorian Adolescent Health Cohort Study (1992-2008). Although we have endeavored to follow best practices, there is little published advice on this, and we have not previously ...
Romaniuk, Helena +2 more
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Comparison of Performance of Data Imputation Methods for Numeric Dataset
Applied Artificial Intelligence, 2019 Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis.
Anil Jadhav +2 more
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