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Abstract:
Motivation: Distinguishing direct from indirect influences is a central
issue in reverse engineering of biological networks because it
facilitates detection and removal of false positive edges. Transitive
reduction is one approach for eliminating edges reflecting indirect
effects but its use in reconstructing cyclic interaction graphs with
true redundant structures is problematic.
Results: We present TRANSWESD, an elaborated variant of
TRANSitive reduction for WEighted Signed Digraphs that overcomes
conceptual problems of existing versions. Major changes and
improvements concern: (i) new statistical approaches for generating
high-quality perturbation graphs from systematic perturbation
experiments; (ii) the use of edge weights (association strengths)
for recognizing true redundant structures; (iii) causal interpretation
of cycles; (iv) relaxed definition of transitive reduction; and
(v) approximation algorithms for large networks. Using standardized
benchmark tests, we demonstrate that our method outperforms
existing variants of transitive reduction and is, despite its conceptual
simplicity, highly competitive with other reverse engineering
methods.
Contact: klamt@mpi-magdeburg.mpg.de
Supplementary information: Supplementary data are available at
Bioinformatics online.
[accessed 2013 July 2nd]
