__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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