12th International Symposium on Health Informatics and Bioinformatics (HIBIT) 2019, İzmir, Türkiye, 17 - 18 Ekim 2019, ss.230
Cellular metabolism includes highly complex processes which are
essential for the survival of any organism. To enlighten mechanism
of many diseases and treatments, it is very important to understand
human metabolic system. Analysis of metabolic system as a network is
crucial and applied in recent years with the help of holistic approaches
over human disease studies. Various methods have been proposed to
identify interactions between nodes in the reactions by the help of
standard graph theory measurements. Available metabolic networks
connect all possible reactant pairs of a reaction to construct metabolite
network. This approach introduces inaccuracy due to connections
between currency metabolites and actual reactants.
In order to increase accuracy we removed the connections between
unrelated molecules. In our project, we applied tanimoto similarity
and flexible most common substructure functions available in
ChemmineR package to construct accurate compound networks.
After constructing more accurate metabolic network, we applied
L-value , a recently developed node importance measure, to
identify critical compounds in human metabolic network.
Keywords: Metabolic Network; Network Analysis; Tanimoto Similarity
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ChemmineR: a compound mining framework for R.
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