Finding Predictive Gene Groups from Microarray Data
|Authors:||Marcel Dettling and Peter Buehlmann
|Published:||In the Journal of Multivariate Analysis, July 2004.
Microarray experiments generate large datasets with expression values for thousands of genes, but not more than a few dozens of samples. A challenging task with these data is to reveal groups of genes which act together and whose collective expression is strongly associated with an outcome variable of interest. To find these groups, we suggest the use of supervised algorithms: these are procedures which use external information about the response variable for grouping the genes. We present Pelora, an algorithm based on PEnalized LOgistic Regression Analysis, that combines gene selection, gene grouping and sample classification in a supervised, simultaneous way. With an empirical study on six different microarray datasets, we show that Pelora identifies gene groups whose expression centroids have very good predictive potential and yield results that can keep up with state-of-the-art classification methods based on single genes. Thus, our gene groups can be beneficial in medical diagnostics and prognostics, but they may also provide more biological insights into gene function and regulation.
Multivariate Analysis (2004), Vol.90, No.1, p. 106-131.
|Download:|| The preprint
is available as PDF(354k)
version with some minor changes is available online from the
webpage of the Journal of Multivariate Analysis. Please note that
full-text access requires a subscription to Science Direct.
|Back / Home||Marcel Dettling, 24.05.2004|