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

Dellinger AE, Nixon AB, Pang H - Cancer Inform (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Elon University, Elon, NC, USA. ; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Caspase pathway validation network in OV. This figure represents the network of patients discovered in testing the caspase pathway in ovarian cancer. Nodes represent patients. The top 200 weighted edges are shown. Weights were determined using α and Pearson correlation coefficients of the integrated data types. Light gray nodes are incorrect integrative method predictions. Medium gray nodes are correct predictions by all data types. Dark gray nodes are correct integrative method predictions and at least one incorrect single data-type prediction.
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f1-cin-suppl.4-2014-001: Caspase pathway validation network in OV. This figure represents the network of patients discovered in testing the caspase pathway in ovarian cancer. Nodes represent patients. The top 200 weighted edges are shown. Weights were determined using α and Pearson correlation coefficients of the integrated data types. Light gray nodes are incorrect integrative method predictions. Medium gray nodes are correct predictions by all data types. Dark gray nodes are correct integrative method predictions and at least one incorrect single data-type prediction.

Mentions: Figure 1 is an example of the network graph of a test set V in the OV dataset. This network graph was used to predict each patient’s stage (early or advanced) in V.


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

Dellinger AE, Nixon AB, Pang H - Cancer Inform (2014)

Caspase pathway validation network in OV. This figure represents the network of patients discovered in testing the caspase pathway in ovarian cancer. Nodes represent patients. The top 200 weighted edges are shown. Weights were determined using α and Pearson correlation coefficients of the integrated data types. Light gray nodes are incorrect integrative method predictions. Medium gray nodes are correct predictions by all data types. Dark gray nodes are correct integrative method predictions and at least one incorrect single data-type prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4125381&req=5

f1-cin-suppl.4-2014-001: Caspase pathway validation network in OV. This figure represents the network of patients discovered in testing the caspase pathway in ovarian cancer. Nodes represent patients. The top 200 weighted edges are shown. Weights were determined using α and Pearson correlation coefficients of the integrated data types. Light gray nodes are incorrect integrative method predictions. Medium gray nodes are correct predictions by all data types. Dark gray nodes are correct integrative method predictions and at least one incorrect single data-type prediction.
Mentions: Figure 1 is an example of the network graph of a test set V in the OV dataset. This network graph was used to predict each patient’s stage (early or advanced) in V.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Elon University, Elon, NC, USA. ; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus