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Biological network comparison via Ipsen-Mikhailov distance | | Giuseppe Jurman
; Samantha Riccadonna
; Roberto Visintainer
; Cesare Furlanello
; | | Date: |
1 Sep 2011 | | Abstract: | Highlighting similarities and differences between networks is an informative
task in investigating many biological processes. Typical examples are detecting
differences between an inferred network and the corresponding gold standard, or
evaluating changes in a dynamic network along time. Although fruitful insights
can be drawn by qualitative or feature-based methods, a distance must be used
whenever a quantitative assessment is required. Here we introduce the
Ipsen-Mikhailov metric for biological network comparison, based on the
difference of the distributions of the Laplacian eigenvalues of the compared
graphs. Being a spectral measure, its focus is on the general structure of the
net so it can overcome the issues affecting local metrics such as the edit
distances. Relation with the classical Matthews Correlation Coefficient (MCC)
is discussed, showing the finer discriminant resolution achieved by the
Ipsen-Mikhailov metric. We conclude with three examples of application in
functional genomic tasks, including stability of network reconstruction as
robustness to data subsampling, variability in dynamical networks and
differences in networks associated to a classification task. | | Source: | arXiv, 1109.0220 | | Services: | Forum | Review | PDF | Favorites | |
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