### Abstract

This paper presents the network-structure search evolutionary algorithm (NSS-EA) for inference of genetic networks by S-systems. NSS-EA efficiently finds multiple different network structures which explain gene-expression time-course data observed in biological experiments. In inference of genetic networks by S-system, we are required to find as many network structures that explain experimentally-observed data as possible. This is because, in general, it is difficult to obtain sufficient time-course data by which we can determine a network structure uniquely. A network structure is determined by whether each system parameter of its S-system is positive, negative or zero. Tominaga et al. and Ueda et al. have proposed methods that repeatedly run real-coded genetic algorithms (GAs) for searching the system parameters of S-system with different random number series in each GA run to obtain multiple different network structures. These methods have two serious problems that the same network structures can be repeatedly found in multiple GA runs and that a biological knowledge that the number of substances interacting with one substance is relatively small is not taken into account. This is because how many and what kind of structures are found by real-coded GAs depend on the random number series used by the Gas. In this paper, we try to solve the above problems by explicitly separating the process of searching network structure, i.e. searching the signs of system parameters of S-system, and that of searching the values of the system parameters. Through some numerical experiments, we show that the proposed method, NSS-EA, can efficiently find more different kinds of network structures than the conventional methods.

Original language | English |
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Title of host publication | 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings |

Publisher | IEEE Computer Society |

Pages | 615-622 |

Number of pages | 8 |

Volume | 1 |

DOIs | |

Publication status | Published - 2003 |

Event | 2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia Duration: Dec 8 2003 → Dec 12 2003 |

### Other

Other | 2003 Congress on Evolutionary Computation, CEC 2003 |
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Country | Australia |

City | Canberra, ACT |

Period | 12/8/03 → 12/12/03 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computational Mathematics

### Cite this

*2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings*(Vol. 1, pp. 615-622). [1299633] IEEE Computer Society. https://doi.org/10.1109/CEC.2003.1299633

**Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by S-system.** / Morishita, Ryohei; Imade, Hiroaki; Ono, Lsao; Ono, Norihiko; Okamoto, Masahiro.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings.*vol. 1, 1299633, IEEE Computer Society, pp. 615-622, 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia, 12/8/03. https://doi.org/10.1109/CEC.2003.1299633

}

TY - GEN

T1 - Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by S-system

AU - Morishita, Ryohei

AU - Imade, Hiroaki

AU - Ono, Lsao

AU - Ono, Norihiko

AU - Okamoto, Masahiro

PY - 2003

Y1 - 2003

N2 - This paper presents the network-structure search evolutionary algorithm (NSS-EA) for inference of genetic networks by S-systems. NSS-EA efficiently finds multiple different network structures which explain gene-expression time-course data observed in biological experiments. In inference of genetic networks by S-system, we are required to find as many network structures that explain experimentally-observed data as possible. This is because, in general, it is difficult to obtain sufficient time-course data by which we can determine a network structure uniquely. A network structure is determined by whether each system parameter of its S-system is positive, negative or zero. Tominaga et al. and Ueda et al. have proposed methods that repeatedly run real-coded genetic algorithms (GAs) for searching the system parameters of S-system with different random number series in each GA run to obtain multiple different network structures. These methods have two serious problems that the same network structures can be repeatedly found in multiple GA runs and that a biological knowledge that the number of substances interacting with one substance is relatively small is not taken into account. This is because how many and what kind of structures are found by real-coded GAs depend on the random number series used by the Gas. In this paper, we try to solve the above problems by explicitly separating the process of searching network structure, i.e. searching the signs of system parameters of S-system, and that of searching the values of the system parameters. Through some numerical experiments, we show that the proposed method, NSS-EA, can efficiently find more different kinds of network structures than the conventional methods.

AB - This paper presents the network-structure search evolutionary algorithm (NSS-EA) for inference of genetic networks by S-systems. NSS-EA efficiently finds multiple different network structures which explain gene-expression time-course data observed in biological experiments. In inference of genetic networks by S-system, we are required to find as many network structures that explain experimentally-observed data as possible. This is because, in general, it is difficult to obtain sufficient time-course data by which we can determine a network structure uniquely. A network structure is determined by whether each system parameter of its S-system is positive, negative or zero. Tominaga et al. and Ueda et al. have proposed methods that repeatedly run real-coded genetic algorithms (GAs) for searching the system parameters of S-system with different random number series in each GA run to obtain multiple different network structures. These methods have two serious problems that the same network structures can be repeatedly found in multiple GA runs and that a biological knowledge that the number of substances interacting with one substance is relatively small is not taken into account. This is because how many and what kind of structures are found by real-coded GAs depend on the random number series used by the Gas. In this paper, we try to solve the above problems by explicitly separating the process of searching network structure, i.e. searching the signs of system parameters of S-system, and that of searching the values of the system parameters. Through some numerical experiments, we show that the proposed method, NSS-EA, can efficiently find more different kinds of network structures than the conventional methods.

UR - http://www.scopus.com/inward/record.url?scp=84901454547&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901454547&partnerID=8YFLogxK

U2 - 10.1109/CEC.2003.1299633

DO - 10.1109/CEC.2003.1299633

M3 - Conference contribution

AN - SCOPUS:84901454547

VL - 1

SP - 615

EP - 622

BT - 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

PB - IEEE Computer Society

ER -