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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

Empirical workflow of TCGA data to directed graph (BN) to undirected graph (JT) to Knowledge (J2K)
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f1-2092351: Empirical workflow of TCGA data to directed graph (BN) to undirected graph (JT) to Knowledge (J2K)

Mentions: Our workflow, as shown in Figure 1, first discretizes the features in the data using MDLPC [19], and selects those that best distinguish the target class via feature selection with the ReliefF [20] algorithm. Then it builds a BN using EBMC [21], and finally it transforms the directed network into a JT [13]. The remaining parts of this section describe these algorithms, including the in-house developed JT creation algorithms.


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)

Empirical workflow of TCGA data to directed graph (BN) to undirected graph (JT) to Knowledge (J2K)
© Copyright Policy
Related In: Results  -  Collection

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

f1-2092351: Empirical workflow of TCGA data to directed graph (BN) to undirected graph (JT) to Knowledge (J2K)
Mentions: Our workflow, as shown in Figure 1, first discretizes the features in the data using MDLPC [19], and selects those that best distinguish the target class via feature selection with the ReliefF [20] algorithm. Then it builds a BN using EBMC [21], and finally it transforms the directed network into a JT [13]. The remaining parts of this section describe these algorithms, including the in-house developed JT creation algorithms.

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