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Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data

Recent method development has included multi-dimensional genomic data algorithms because such methods have more accurately predicted clinical phenotypes related to disease. This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The met...

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Detalles Bibliográficos
Autores principales: Dellinger, Andrew E, Nixon, Andrew B, Pang, Herbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125381/
https://www.ncbi.nlm.nih.gov/pubmed/25125969
http://dx.doi.org/10.4137/CIN.S13634
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author Dellinger, Andrew E
Nixon, Andrew B
Pang, Herbert
author_facet Dellinger, Andrew E
Nixon, Andrew B
Pang, Herbert
author_sort Dellinger, Andrew E
collection PubMed
description Recent method development has included multi-dimensional genomic data algorithms because such methods have more accurately predicted clinical phenotypes related to disease. This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The methodology of this analysis, graph-based semi-supervised learning, detects pathways that improve prediction of a dichotomous variable, which in this study is cancer stage. This analysis integrates genome-level gene expression, methylation, and single nucleotide polymorphism (SNP) data in serous cystadenocarcinoma (OV) and colon adenocarcinoma (COAD). The top 10 ranked predictive pathways in COAD and OV were biologically relevant to their respective cancer stages and significantly enhanced prediction accuracy and area under the ROC curve (AUC) when compared to single data-type analyses. This method is an effective way to simultaneously predict binary clinical phenotypes and discover their biological mechanisms.
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spelling pubmed-41253812014-08-14 Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data Dellinger, Andrew E Nixon, Andrew B Pang, Herbert Cancer Inform Technical Advance Recent method development has included multi-dimensional genomic data algorithms because such methods have more accurately predicted clinical phenotypes related to disease. This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The methodology of this analysis, graph-based semi-supervised learning, detects pathways that improve prediction of a dichotomous variable, which in this study is cancer stage. This analysis integrates genome-level gene expression, methylation, and single nucleotide polymorphism (SNP) data in serous cystadenocarcinoma (OV) and colon adenocarcinoma (COAD). The top 10 ranked predictive pathways in COAD and OV were biologically relevant to their respective cancer stages and significantly enhanced prediction accuracy and area under the ROC curve (AUC) when compared to single data-type analyses. This method is an effective way to simultaneously predict binary clinical phenotypes and discover their biological mechanisms. Libertas Academica 2014-07-28 /pmc/articles/PMC4125381/ /pubmed/25125969 http://dx.doi.org/10.4137/CIN.S13634 Text en © 2014 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article published under the Creative Commons CC-BY-NC 3.0 License.
spellingShingle Technical Advance
Dellinger, Andrew E
Nixon, Andrew B
Pang, Herbert
Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title_full Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title_fullStr Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title_full_unstemmed Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title_short Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data
title_sort integrative pathway analysis using graph-based learning with applications to tcga colon and ovarian data
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125381/
https://www.ncbi.nlm.nih.gov/pubmed/25125969
http://dx.doi.org/10.4137/CIN.S13634
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