Missing value estimation methods for DNA microarrays.
作者： Troyanskaya Olga , Cantor Michael , Shelock Gavin , Brown Pat , Hastie Trevor
DOI: 10.1093/BIOINFORMATICS/17.6.520
关键词:
摘要: 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. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to …
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