Missing value estimation methods for DNA microarrays

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Missing value estimation methods for DNA microarrays (2001)

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by Olga Troyanskaya , Michael Cantor , Gavin Sherlock , Pat Brown , Trevor Hastie , Robert Tibshirani , David Botstein , Russ B. Altman

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474 - 24 self

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@MISC{Troyanskaya01missingvalue,
    author = {Olga Troyanskaya and Michael Cantor and Gavin Sherlock and Pat Brown and Trevor Hastie and Robert Tibshirani and David Botstein and Russ B. Altman},
    title = {Missing value estimation methods for DNA microarrays},
    year = {2001}
}

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Abstract

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 increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.

Keyphrases

dna microarrays value estimation method data set hierarchical clustering complete matrix expression value many algorithm gene expression microarray experiment incomplete data set gene array value gene expression analysis

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