### Resumé

Originalsprog | Engelsk |
---|---|

Titel | GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion |

Publikationsdato | 2013 |

Sider | 43-44 |

ISBN (Trykt) | 9781450319645 |

DOI | |

Status | Udgivet - 2013 |

### Fingeraftryk

### Citer dette

*GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion*(s. 43-44) https://doi.org/10.1145/2464576.2464600

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*GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion.*s. 43-44. https://doi.org/10.1145/2464576.2464600

**NABEECO : Biological network alignment with bee colony optimization algorithm.** / Ibragimov, R.; Martens, Johan; Guo, Jian-Ying; Baumbach, Jan.

Publikation: Bidrag til bog/antologi/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - NABEECO

T2 - Biological network alignment with bee colony optimization algorithm

AU - Ibragimov, R.

AU - Martens, Johan

AU - Guo, Jian-Ying

AU - Baumbach, Jan

PY - 2013

Y1 - 2013

N2 - Motivation: A growing number of biological networks of ever increasing sizes are becoming available nowadays, making the ability to solve Network Alignment of primer importance. However, computationally the problem is hard for data sets of real-world sizes. Results: we developed NABEECO, a novel and robust Network Alignment heuristic based on Bee Colony Optimization. We use the so-called Graph Edit Distance (GED) as optimization criterion, which is defined as the minimal amount of edge and node modifications necessary to transform one graph into another. We compare NABEECO on a set of protein-protein interaction networks to the current state of the art tool for biological networks, MI-GRAAL. Conclusion: We present the first Bee Colony Optimization algorithm for biological Network Alignment. NABEECO, in contrast to many other tools, can be applied to all kinds of networks and allows incorporating prior knowledge about node/edge similarity, though this is not required a priori. NABEECO together with a more detailed description and all data sets used are publicly available at http://nabeeco.mpi- inf.mpg.de.

AB - Motivation: A growing number of biological networks of ever increasing sizes are becoming available nowadays, making the ability to solve Network Alignment of primer importance. However, computationally the problem is hard for data sets of real-world sizes. Results: we developed NABEECO, a novel and robust Network Alignment heuristic based on Bee Colony Optimization. We use the so-called Graph Edit Distance (GED) as optimization criterion, which is defined as the minimal amount of edge and node modifications necessary to transform one graph into another. We compare NABEECO on a set of protein-protein interaction networks to the current state of the art tool for biological networks, MI-GRAAL. Conclusion: We present the first Bee Colony Optimization algorithm for biological Network Alignment. NABEECO, in contrast to many other tools, can be applied to all kinds of networks and allows incorporating prior knowledge about node/edge similarity, though this is not required a priori. NABEECO together with a more detailed description and all data sets used are publicly available at http://nabeeco.mpi- inf.mpg.de.

UR - http://www.scopus.com/inward/record.url?scp=84882327497&partnerID=8YFLogxK

U2 - 10.1145/2464576.2464600

DO - 10.1145/2464576.2464600

M3 - Article in proceedings

SN - 9781450319645

SP - 43

EP - 44

BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion

ER -