Dataset Information

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 protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes.

Conclusions

The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

SUBMITTER: Park C

PROVIDER: S-EPMC3908883 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

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

Park Chihyun C Ahn Jaegyoon J Kim Hyunjin H Park Sanghyun S

PloS one 20140131 1

<h4>Background</h4>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 o ...[more]

PMID: 24497942

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Dataset Information

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

Background

Results

Conclusions

Publications

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

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