Semi-supervised learning improves gene expression-based prediction of cancer recurrence.

ABSTRACT:

Motivation

Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequently, a large number of microarray data that do not have sufficient follow-up information are disregarded. To fully leverage all of the precious data in public databases, we turned to a semi-supervised learning technique, low density separation (LDS).

Results

Using a clinically important question of predicting recurrence risk in colorectal cancer patients, we demonstrated that (i) semi-supervised classification improved prediction accuracy as compared with the state of the art sup

SUBMITTER: Shi M 

PROVIDER: S-EPMC3198572 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature