A note on orientations of mixed graphs
Discrete Applied Mathematics
Bioinformatics
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Digraphs: Theory, Algorithms and Applications
Digraphs: Theory, Algorithms and Applications
Improved orientations of physical networks
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Exploiting bounded signal flow for graph orientation based on cause-effect pairs
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
Approximation algorithms for orienting mixed graphs
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
Approximation algorithms and hardness results for shortest path based graph orientations
CPM'12 Proceedings of the 23rd Annual conference on Combinatorial Pattern Matching
Improved approximation for orienting mixed graphs
SIROCCO'12 Proceedings of the 19th international conference on Structural Information and Communication Complexity
Steiner forest orientation problems
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
| Hi-index | 0.00 |
In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This problem is NP-complete and no approximation algorithms are known for it. It arises in the context of analyzing physical networks of protein-protein and protein-dna interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ilp formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified algorithmic variants that do not use information on edge directions. The obtained orientations can lead to better understanding of the structure and function of the network.