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Integrating Gene Regulatory Networks to identify cancer-specific genes.

Bo V, Tucker A - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions.The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters.Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Brunel University, London, UK.

ABSTRACT
Consensus approaches have been widely used to identify Gene Regulatory Networks (GRNs) that are common to multiple studies. However, in this research we develop an application that semi-automatically identifies key mechanisms that are specific to a particular set of conditions. We analyse four different types of cancer to identify gene pathways unique to each of them. To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions. The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters. Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators. Results show that unique genes and sub-networks can be reliably identified and that they reflect key mechanisms that are fundamental to the cancer types under study.

No MeSH data available.


Related in: MedlinePlus

Logic Application interface.
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f3-2082746: Logic Application interface.

Mentions: Finally, Figure 3 shows the Logic Application interface. The example allows the user to visualize the unique sub-networks and the list of related genes that study 1 AND 4 have in common but do not appear in study 2.


Integrating Gene Regulatory Networks to identify cancer-specific genes.

Bo V, Tucker A - AMIA Jt Summits Transl Sci Proc (2015)

Logic Application interface.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525222&req=5

f3-2082746: Logic Application interface.
Mentions: Finally, Figure 3 shows the Logic Application interface. The example allows the user to visualize the unique sub-networks and the list of related genes that study 1 AND 4 have in common but do not appear in study 2.

Bottom Line: To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions.The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters.Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Brunel University, London, UK.

ABSTRACT
Consensus approaches have been widely used to identify Gene Regulatory Networks (GRNs) that are common to multiple studies. However, in this research we develop an application that semi-automatically identifies key mechanisms that are specific to a particular set of conditions. We analyse four different types of cancer to identify gene pathways unique to each of them. To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions. The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters. Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators. Results show that unique genes and sub-networks can be reliably identified and that they reflect key mechanisms that are fundamental to the cancer types under study.

No MeSH data available.


Related in: MedlinePlus