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Shinichi Kikuchi
Researcher at Keio University
Publications - 14
Citations - 657
Shinichi Kikuchi is an academic researcher from Keio University. The author has contributed to research in topics: Genetic programming & Translational efficiency. The author has an hindex of 7, co-authored 14 publications receiving 643 citations. Previous affiliations of Shinichi Kikuchi include National Institute of Advanced Industrial Science and Technology.
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Dynamic modeling of genetic networks using genetic algorithm and S-system
TL;DR: A unified extension of the basic method to predict not only the network structure but also its dynamics using a Genetic Algorithm and an S-system formalism is proposed and successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.
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Reverse engineering of biochemical equations from time-course data by means of genetic programming.
TL;DR: A technique to predict an equation using genetic programming that can search topology and numerical parameters of mathematical expression simultaneously and can be applied to identify metabolic reactions from observable time-courses is presented.
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Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks
Masahiro Sugimoto,Shinichi Kikuchi,Masanori Arita,Masanori Arita,Tomoyoshi Soga,Takaaki Nishioka,Masaru Tomita +6 more
TL;DR: It is suggested that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.
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Kinetic simulation of signal transduction system in hippocampal long-term potentiation with dynamic modeling of protein phosphatase 2A
Shinichi Kikuchi,Kenji Fujimoto,Noriyuki Kitagawa,Taro Fuchikawa,Michiko Abe,Kotaro Oka,Kohtaro Takei,Masaru Tomita +7 more
TL;DR: It is shown that another mechanism could introduce bistable behavior by adding dynamic reactions of PP2A, and this mechanism played an essential role, rather than the activation of protein kinase C (PKC) as documented in the conventional model.
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Parameter estimation for stiff equations of biosystems using radial basis function networks
TL;DR: This work explored a learning technique that uses radial basis function networks (RBFN) to achieve a parameter estimation for biochemical models and found that the calculation time decreased by more than 50% and the convergence rate increased from 60% to 90%.