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Dealing with missing values in large-scale studies: microarray data imputation and beyond
2009
Briefings in Bioinformatics
High-throughput biotechnologies, such as gene expression microarrays or mass-spectrometry-based proteomic assays, suffer from frequent missing values due to various experimental reasons. Since the missing data points can hinder downstream analyses, there exists a wide variety of ways in which to deal with missing values in large-scale data sets. Nowadays, it has become routine to estimate (or impute) the missing values prior to the actual data analysis. After nearly a decade since the
doi:10.1093/bib/bbp059
pmid:19965979
fatcat:bnj6czor2rbhxdzc5noaodxqcm