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Querying KEGG pathways in logic

Authors: Kazi Zakia Sultana; Anupam Bhattacharjee; Hasan Jamil;

Querying KEGG pathways in logic

Abstract

Understanding the interaction patterns among biological entities in a pathway can potentially reveal the role of the entities in biological systems. Although considerable effort has been contributed to this direction, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways satisfying a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways using a novel and generic subgraph isomorphism computation technique. We develop a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It allows editing KGML represented query pathways and returns all isomorphic patterns in KEGG pathways satisfying a given query condition for further analysis.

Related Organizations
Keywords

Models, Biological, User-Computer Interface, Logistic Models, Protein Interaction Mapping, Data Mining, Database Management Systems, Computer Simulation, Databases, Protein, Algorithms, Software, Signal Transduction

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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