Bioinspired Computational Approach to Missing Value Estimation
نویسندگان
چکیده
منابع مشابه
Missing Value Estimation In DNA Microarray – A Fuzzy Approach
DNA microarray technology which is used in molecular biology, allows for the observation of expression levels of thousands of genes under a variety of conditions. The analysis of microarray data has been successfully applied in a number of studies over a broad range of biological disciplines. Now it is very unfortunate that various microarray experiments generate data sets containing missing va...
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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 Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values....
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Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techn...
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ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2018
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2018/9457821