Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression

Lei Ding, Daniel J. McDonald

Abstract: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene–gene relationships and predicting well is desirable.