Network comparison by using network topological features.

Question

[Bioinformatist Newbie]

I want to find disease drug relationship by using network based approaches. Up til now I was using individual network properties like Strongly connected components (SCC), Betweeness Centrality, transitivity, network diameter and Miniminum dominating set (MDS) but none of these characteristics are yielding the results I am interested in. As example on which I am trying this idea are known (I know the drug causing the phenotype reversion in Gene expression).

Can anybody devise me some other network topology properties like I mentioned, which can be use to compare networks inferred from Gene Expression data?

Secondly I am thinking about combining mentioned features to train the random forest for classification, is it a good idea? If yes, then do share your your experience.

Thanks

Answer 1

[Girolamo]

Regarding the first question, you can have a look for algorithm for exact and quasi-exact network (sub)isomorphism (VF2 is one of the most used for exact subgraph isomorphism and is already implemented in networkx library for python), or network alignment (http://www.ncbi.nlm.nih.gov/pubmed/22234340).

[pld]

The link you provided doesn't work, can you please correct this?

[Bioinformatist Newbie]

Just remove the last bracket and it'll work. http://www.ncbi.nlm.nih.gov/pubmed/22234340

Answer 2

[Zhilong Jia]

Another one, Network fingerprint: a knowledge-based characterization of biomedical networks may be helpful.