### Background Information
Different biological networks have rich information to explore, including signaling pathways, conserved function modules, regulatory relationships, and species evolution. For example, the protein-protein interaction (PPI) data in bacteria, worm, fly and human, may be used with the network alignment approach to determine whether the conserved sequences in different species have similar functions. It can be a means to study human disease using the model organism.
Another **possible** field BNMatch could be applied to is drug repurposing, represented as an effective drug discovery strategy from existing drugs[2-4]. As PPIs offers an effective way towards elucidating the mechanisms of viral infection and BNMatch takes both sequence and topological information into account, it's reasonable for its application in this field, and I' m currently working on it.
Form the efficiency aspect, the network is considerably large sometimes, and BNMatch has provided a GPU-assisted computing option, which allows users to use their GPU and save a significant portion of the time.
### BNMatch Profile
BNMatch stands for biological network matcher, which means networks sharing the comparable topology and nodes' data
such as protein-protein interaction networks, gene networks could be its targets.
Work with Cytoscape 3.8 or above.
1. Side by side layout to show your index network and its corresponding part in the target network.
2. Optimized hybrid Hungarian and Greedy algorithm to align globally
3. Edge Correctness and different scores to evaluate your mapping result
4. Different colors and shape to mark the mapping nodes
5. The Preinstall of BLAST+ is required to get the similarity matrix.
6. GPU acceleration is allowed.
There is another BNMatch app developed by my mentor's team before, but it's no longer supported in Cytoscape3.x, and the
principal differences between the two apps:
1. New algorithm(HGA) to map nodes
2. Adaptive Hungarian matrix selection within each iteration
3. GPU computing
For more information, please refer to the tutorial.
Please feel free to contact me by email.
CS school at Shanghai University
.Xie J, Xiang C, Ma J, et al. An Adaptive Hybrid Algorithm for Global Network Alignment. IEEE/ACM Trans Comput Biol Bioinform. 2016;13(3):483-493. doi:10.1109/TCBB.2015.2465957
Cheng, F. In silico oncology drug repositioning and polypharmacology.
Methods Mol. Biol. 1878, 243–261 (2019).
Cheng, F., Hong, H., Yang, S. & Wei, Y. Individualized network-based drug
repositioning infrastructure for precision oncology in the panomics era. Brief
Bioinformatics 18, 682–697 (2017).
Cheng, F., Murray, J. L. & Rubin, D. H. Drug repurposing: new treatments for
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