Single-gene classifiers and outlier-resistant detection of
differential expression for two-group and survival problems.
Description
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.