Evaluation of Missing Value Estimation for Microarray Data
نویسندگان
چکیده
Publisher: School of Statistics, Renmin University China, Journal: Journal Data Science, Title: Evaluation Missing Value Estimation for Microarray Data, Authors: Danh V. Nguyen, Naisyin Wang, Raymond J. Carroll
منابع مشابه
Evaluation of Missing Value Estimation for Microarray Data
Microarray gene expression data contains missing values (MVs). However, some methods for downstream analyses, including some prediction tools, require a complete expression data matrix. Current methods for estimating the MVs include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation (imputation) methods depends on the actual gene expression has not been thoroughly inv...
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Microarrays have unique ability to probe thousands of genes at a time that makes it a useful tool for variety of applications, ranging from diagnosis to drug discovery. However, data generated by microarrays often contains multiple missing gene expressions that affect the subsequent analysis, as most of the times these missing values are ignored. In this paper we have analyzed how accurate esti...
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Motivation: Microarray data is used in a range of application areas in biology, though often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible prior to using these algorithms. While many imputation algo...
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MOTIVATION Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algo...
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ژورنال
عنوان ژورنال: Journal of data science
سال: 2021
ISSN: ['1680-743X', '1683-8602']
DOI: https://doi.org/10.6339/jds.2004.02(4).170