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Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908883/ https://www.ncbi.nlm.nih.gov/pubmed/24497942 http://dx.doi.org/10.1371/journal.pone.0086309 |
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author | Park, Chihyun Ahn, Jaegyoon Kim, Hyunjin Park, Sanghyun |
author_facet | Park, Chihyun Ahn, Jaegyoon Kim, Hyunjin Park, Sanghyun |
author_sort | Park, Chihyun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3908883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39088832014-02-04 Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning Park, Chihyun Ahn, Jaegyoon Kim, Hyunjin Park, Sanghyun PLoS One Research Article 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. Public Library of Science 2014-01-31 /pmc/articles/PMC3908883/ /pubmed/24497942 http://dx.doi.org/10.1371/journal.pone.0086309 Text en © 2014 Park et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Park, Chihyun Ahn, Jaegyoon Kim, Hyunjin Park, Sanghyun Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title | Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title_full | Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title_fullStr | Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title_full_unstemmed | Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title_short | Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning |
title_sort | integrative gene network construction to analyze cancer recurrence using semi-supervised learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908883/ https://www.ncbi.nlm.nih.gov/pubmed/24497942 http://dx.doi.org/10.1371/journal.pone.0086309 |
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