PPINA: Alignment of protein interaction networks by integer quadratic programming
Reference#
- Zhenping Li, Yong Wang, Shihua Zhang, Xiang-Sun Zhang, Luonan Chen. Alignment of protein interaction networks by integer quadratic programming. In Proceedings of 28th Annual International Conference of IEEE Engineering in Medicine and Biology Society, 1:5527-5530, 2006.
Supplementary Materials#
Fig. 1. An tutorial network alignment example from PathBLAST plug-in of Cytoscape software. #
The corresponding relation of labels and their number order of Net A.
A1----A A2----B A3----C A4----D A5----E A6----F A7----G A8----H A9----I A10---J A11---K A12---L
The corresponding relation of label and their number order of Net B.
B1-----AA B2-----BB B3-----CC B4-----DD B5-----HH B6-----MM B7-----ZZ B8-----NN B9-----QQ B10----JJ B11----OO B12----WW
The adjacent matrices of the two networks A and B and their similarity matrix S
A =[ 0 0.10 0.70 0.01 0 0 0 0 0 0 0 0
0.10 0 0 0.30 0 0 0.01 0 0.02 0 0 0
0.70 0 0 0 0 0.20 0.01 0 0 0 0 0
0.01 0.30 0 0 0.2 0 0.01 0 0 0 0 0 0
0 0 0 0.20 0 0 0 0 0.01 0 0 0
0 0 0.20 0 0 0 0 0 0 0 0 0
0 0.01 0.01 0.01 0 0 0 0.70 0 0 0 0
0 0 0 0 0 0 0.70 0 0 0 0 0
0 0.02 0 0 0.01 0 0 0 0 0.30 0.01 0.60
0 0 0 0 0 0 0 0 0.30 0 0 0
0 0 0 0 0 0 0 0 0.01 0 0 0
0 0 0 0 0 0 0 0 0.60 0 0 0 ];
B =[ 0 0 0 0 0 0 0.01 0.20 0.10 0 0 0
0 0 0 0.01 0.70 0 0 0 0.70 0.01 0 0
0 0 0 0 0 0.02 0.20 0.10 0 0 0 0
0 0.01 0 0 0 0 0 0 0 0 0.10 0.01
0 0.70 0 0 0 0 0 0 0 0 0 0
0 0 0.02 0 0 0 0 0 0 0 0 0
0.01 0 0.20 0 0 0 0 0 0 0 0 0
0.20 0 0.10 0 0 0 0 0 0 0 0 0
0.10 0.70 0 0 0 0 0 0 0 0 0 0
0 0.01 0 0 0 0 0 0 0 0 0 0
0 0 0 0.10 0 0 0 0 0 0 0 0
0 0 0 0.01 0 0 0 0 0 0 0 0 ] ;
S =[ 0.1 0.1 0.1 0.8 0.5 0.1 0.1 0.8 0.8 0.1 0.1 0.1
0.1 0.1 0.8 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.1 0.1
0.1 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.1 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.1 0.8 0.1 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.8 0.1
0.1 0.1 0.1 0.1 0.8 0.1 0.1 0.8 0.1 0.1 0.1 0.1
0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.8 0.1
0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.1 0.1 0.1 0.8 0.1 0.8 0.8 0.1 0.1 0.1 0.1 0.8
0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.8
0.8 0.1 0.1 0.1 0.1 0.8 0.1 0.1 0.8 0.1 0.1 0.8
0.1 0.1 0.8 0.8 0.1 0.1 0.1 0.1 0.1 0.1 0.8 0.8 ] ;
Fig. 2. The simulated example of two directed networks#
The adjacent matrices of the two networks A and B
A =[ 0 1 1 1 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 1 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 ];
B =[ 0 1 1 1 1 1 1 0 0 0 0 0
0 0 0 0 0 0 0 1 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 1 0 0 0
0 0 0 0 0 0 1 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 ];
Similarity matrix
S =[ 0.8 0.2 0.9 0.3 0.5 0.5 0.6 0.1 0.3 0.2 0.1 0.4
0.2 0.6 0.3 0.4 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.4
0.3 0.2 0.7 0.2 0.3 0.7 0.2 0.3 0.3 0.3 0.3 0.1
0.1 0.2 0.3 0.8 0.2 0.4 0.5 0.3 0.8 0.2 0.2 0.3
0.2 0.2 0.3 0.4 0.6 0.4 0.3 0.2 0.3 0.2 0.4 0.2
0.4 0.3 0.3 0.2 0.2 0.4 0.2 0.1 0.1 0.6 0.1 0.1
0.1 0.2 0.3 0.3 0.3 0.3 0.2 0.2 0.3 0.3 0.4 0.4
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.2 0.3 0.2 0.4 0.4 0.4 0.3 0.3 0.5 0.4 0.6 0.4
0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.2 0.2 0.2
0.2 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.2 0.4 0.4
0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.2 0.2 0.3 0.4 0.5
0.2 0.3 0.1 0.4 0.1 0.5 0.2 0.2 0.1 0.1 0.2 0.2 ];
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