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Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis

For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially exp...

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Detalles Bibliográficos
Autores principales: He, Li, Wang, Yuelong, Yang, Yongning, Huang, Liqiu, Wen, Zhining
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4122106/
https://www.ncbi.nlm.nih.gov/pubmed/25126556
http://dx.doi.org/10.1155/2014/424509
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author He, Li
Wang, Yuelong
Yang, Yongning
Huang, Liqiu
Wen, Zhining
author_facet He, Li
Wang, Yuelong
Yang, Yongning
Huang, Liqiu
Wen, Zhining
author_sort He, Li
collection PubMed
description For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.
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spelling pubmed-41221062014-08-14 Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis He, Li Wang, Yuelong Yang, Yongning Huang, Liqiu Wen, Zhining Biomed Res Int Research Article For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes. Hindawi Publishing Corporation 2014 2014-07-14 /pmc/articles/PMC4122106/ /pubmed/25126556 http://dx.doi.org/10.1155/2014/424509 Text en Copyright © 2014 Li He et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
He, Li
Wang, Yuelong
Yang, Yongning
Huang, Liqiu
Wen, Zhining
Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title_full Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title_fullStr Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title_full_unstemmed Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title_short Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis
title_sort identifying the gene signatures from gene-pathway bipartite network guarantees the robust model performance on predicting the cancer prognosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4122106/
https://www.ncbi.nlm.nih.gov/pubmed/25126556
http://dx.doi.org/10.1155/2014/424509
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