Akademik Veri Yönetim Sistemi
7th International Molecular Biology and Biotechnology Congress, Konya, Türkiye, 25 - 27 Nisan 2018, ss.160
The methods like Degree Centrality, Betweenness Centrality and Closeness Centra-
lity provide superficial information about structure of the complex networks, more-
over they are impractical for detecting important nodes. Although there are various
approaches which give deep knowledge to measure the significance of nodes by
combining fundamental network topology parameters such as clustering coefficient
and node neighbourhoods, they fail to properly identify bridge nodes. L-value is
a recently developed measure which can detect significance of nodes in complex
networks based on not only local information but also considering the importance
of bridge nodes. Proposed approach considers total number of triangles in network,
degree of nodes and their neighbours. In light of this, we implemented aforementio-
ned L-value method in human metabolic network to reveal significant nodes, i.e. cri-
tical compounds. Our findings has potential to provide novel and insightful aspect on
gene expression profile analysis. By our method, DEG lists derived from RNA-Seq
or microarray studies can be revisited in terms of their impact on key compounds.
Keywords: Metabolic network, Network topology, DEG, RNA-Seq