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Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.

Pineda AL, Gopalakrishnan V - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data.Our workflow has three steps, preprocessing (discretization and feature selection), construction of a Bayesian network and, its subsequent transformation into a Junction tree.We found relevant cliques of methylated sites that are junctions of the network along with potential methylation biomarkers in the lung cancer pathogenesis.

View Article: PubMed Central - PubMed

Affiliation: The PRoBE Lab, Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA.

ABSTRACT
In this era of precision medicine, understanding the epigenetic differences in lung cancer subtypes could lead to personalized therapies by possibly reversing these alterations. Traditional methods for analyzing microarray data rely on the use of known pathways. We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data. Our workflow has three steps, preprocessing (discretization and feature selection), construction of a Bayesian network and, its subsequent transformation into a Junction tree. We used data from the Cancer Genome Atlas to perform preliminary analyses of this J2K framework. We found relevant cliques of methylated sites that are junctions of the network along with potential methylation biomarkers in the lung cancer pathogenesis.

No MeSH data available.


Related in: MedlinePlus

EBMC-generated BN model for the classification task ADCtumor vs SCCtumor.
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f2-2092351: EBMC-generated BN model for the classification task ADCtumor vs SCCtumor.

Mentions: The structure of the BN created using the EBMC algorithms applied to the data is shown in Figure 2. As expected, there are multiple connections between the target node (class node) and the rest of the selected nodes. There are two fairly distinct clusters of 10 interconnected methylated sites (see Figure 2). The network topology produced by EBMC is an augmented naïve Bayes (ANB) structure. The corresponding gene IDs to where those methylation sites are located is used after this step (i.e. methylation site cg18515587 is located in gene SELENBP1).


Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.

Pineda AL, Gopalakrishnan V - AMIA Jt Summits Transl Sci Proc (2015)

EBMC-generated BN model for the classification task ADCtumor vs SCCtumor.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525224&req=5

f2-2092351: EBMC-generated BN model for the classification task ADCtumor vs SCCtumor.
Mentions: The structure of the BN created using the EBMC algorithms applied to the data is shown in Figure 2. As expected, there are multiple connections between the target node (class node) and the rest of the selected nodes. There are two fairly distinct clusters of 10 interconnected methylated sites (see Figure 2). The network topology produced by EBMC is an augmented naïve Bayes (ANB) structure. The corresponding gene IDs to where those methylation sites are located is used after this step (i.e. methylation site cg18515587 is located in gene SELENBP1).

Bottom Line: We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data.Our workflow has three steps, preprocessing (discretization and feature selection), construction of a Bayesian network and, its subsequent transformation into a Junction tree.We found relevant cliques of methylated sites that are junctions of the network along with potential methylation biomarkers in the lung cancer pathogenesis.

View Article: PubMed Central - PubMed

Affiliation: The PRoBE Lab, Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA.

ABSTRACT
In this era of precision medicine, understanding the epigenetic differences in lung cancer subtypes could lead to personalized therapies by possibly reversing these alterations. Traditional methods for analyzing microarray data rely on the use of known pathways. We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data. Our workflow has three steps, preprocessing (discretization and feature selection), construction of a Bayesian network and, its subsequent transformation into a Junction tree. We used data from the Cancer Genome Atlas to perform preliminary analyses of this J2K framework. We found relevant cliques of methylated sites that are junctions of the network along with potential methylation biomarkers in the lung cancer pathogenesis.

No MeSH data available.


Related in: MedlinePlus