__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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