Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

ABSTRACT:

Background

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence.

Results

In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated p

SUBMITTER: Park C 

PROVIDER: S-EPMC3908883 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature