Results 21 to 30 of about 131,123 (260)
Scientific Reports, 2021 For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required.
Aditya Dubey, Akhtar Rasool
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An Intelligent Missing Data Imputation Techniques: A Review
JOIV: International Journal on Informatics Visualization, 2022 The incomplete dataset is an unescapable problem in data preprocessing that primarily machine learning algorithms could not employ to train the model.
Kimseth Seu, Mi-Sun Kang, HwaMin Lee
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Transformed Distribution Matching for Missing Value Imputation
CoRR, 2023 We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the ...
He Zhao 0001 +3 more
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Scientific Reports, 2023 The problem of missing data, particularly for dichotomous variables, is a common issue in medical research. However, few studies have focused on the imputation methods of dichotomous data and their performance, as well as the applicability of these ...
Yingfeng Ge, Zhiwei Li, Jinxin Zhang
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Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
BMC Bioinformatics, 2022 When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets.
Jonathan P. Dekermanjian +4 more
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Dimensionality reduction with missing values imputation
CoRR, 2017 In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous data that limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation's method.
Rania Mkhinini Gahar +3 more
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Journal of Big Data, 2021 A missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study’s statistical power or result in inaccurate estimations and conclusions.
Heru Nugroho +2 more
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MIGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
Applied Sciences, 2019 This paper addresses the problem of missing data in food composition databases (FCDBs). The missing data can be either for selected foods or for specific components only. Most often, the problem is solved by human experts subjectively borrowing data from
Gordana Ispirova +3 more
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A Comparative Study of Various Methods for Handling Missing Data in UNSODA
Agriculture, 2021 UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e.
Yingpeng Fu, Hongjian Liao, Longlong Lv
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Missing Value Imputation Methods for Electronic Health Records
IEEE Access, 2023 Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format. Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with ...
Konstantinos Psychogyios +3 more
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