Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.
|Author||Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph]|
|Bioconductor views||BiomedicalInformatics Classification DifferentialExpression GeneExpression Survival|
|Maintainer||Mark Pinese <[email protected]>|
|License||EPL (>= 1.0)|
|Package repository||View on Bioconductor|
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