### Abstract

We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.

Original language | English |
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Article number | 957620 |

Journal | The Scientific World Journal |

Volume | 2012 |

DOIs | |

Publication status | Published - Dec 3 2012 |

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### All Science Journal Classification (ASJC) codes

- Biochemistry, Genetics and Molecular Biology(all)
- Environmental Science(all)

### Cite this

*The Scientific World Journal*,

*2012*, [957620]. https://doi.org/10.1100/2012/957620

**Network completion using dynamic programming and least-squares fitting.** / Nakajima, Natsu; Tamura, Takeyuki; Yamanishi, Yoshihiro; Horimoto, Katsuhisa; Akutsu, Tatsuya.

Research output: Contribution to journal › Article

*The Scientific World Journal*, vol. 2012, 957620. https://doi.org/10.1100/2012/957620

}

TY - JOUR

T1 - Network completion using dynamic programming and least-squares fitting

AU - Nakajima, Natsu

AU - Tamura, Takeyuki

AU - Yamanishi, Yoshihiro

AU - Horimoto, Katsuhisa

AU - Akutsu, Tatsuya

PY - 2012/12/3

Y1 - 2012/12/3

N2 - We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.

AB - We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.

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U2 - 10.1100/2012/957620

DO - 10.1100/2012/957620

M3 - Article

C2 - 23213307

AN - SCOPUS:84870197769

VL - 2012

JO - The Scientific World Journal

JF - The Scientific World Journal

SN - 2356-6140

M1 - 957620

ER -