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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

ROC curves for OV pathway Maturity Onset Diabetes of the Young.Notes: ROC curves for single data type analyses SNP (dashed), gene expression (dotted), and methylation level (dot-dash), and for the three data type analysis (solid).
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f2-cin-suppl.4-2014-001: ROC curves for OV pathway Maturity Onset Diabetes of the Young.Notes: ROC curves for single data type analyses SNP (dashed), gene expression (dotted), and methylation level (dot-dash), and for the three data type analysis (solid).

Mentions: Figure 2 demonstrates this fact for “maturity onset diabetes of the young” (Table 4). This pathway is a clear example of the relationship between exemplary prediction and underlying biology with regard to cancer stage. This top pathway in AUC and accuracy is known to advance ovarian cancer stage.46


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

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

ROC curves for OV pathway Maturity Onset Diabetes of the Young.Notes: ROC curves for single data type analyses SNP (dashed), gene expression (dotted), and methylation level (dot-dash), and for the three data type analysis (solid).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.4-2014-001: ROC curves for OV pathway Maturity Onset Diabetes of the Young.Notes: ROC curves for single data type analyses SNP (dashed), gene expression (dotted), and methylation level (dot-dash), and for the three data type analysis (solid).
Mentions: Figure 2 demonstrates this fact for “maturity onset diabetes of the young” (Table 4). This pathway is a clear example of the relationship between exemplary prediction and underlying biology with regard to cancer stage. This top pathway in AUC and accuracy is known to advance ovarian cancer stage.46

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