Sugimoto M - Search Results

1

Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease.

Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K, Ikeda S, Hirayama A, Yamamoto T, Yoshida H, Otsuka M, Tsuji S, Yatomi Y, Sakuragawa T, Watanabe H, Nihei K, Saito T, Kawata S, Suzuki H, Tomita M, Suematsu M. Soga T, et al. Among authors: sugimoto m. J Hepatol. 2011 Oct;55(4):896-905. doi: 10.1016/j.jhep.2011.01.031. Epub 2011 Feb 18. J Hepatol. 2011. PMID: 21334394 Clinical Trial.

2

Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.

Sugimoto M, Kikuchi S, Arita M, Soga T, Nishioka T, Tomita M. Sugimoto M, et al. Anal Chem. 2005 Jan 1;77(1):78-84. doi: 10.1021/ac048950g. Anal Chem. 2005. PMID: 15623281

3

Reverse engineering of biochemical equations from time-course data by means of genetic programming.

Sugimoto M, Kikuchi S, Tomita M. Sugimoto M, et al. Biosystems. 2005 May;80(2):155-64. doi: 10.1016/j.biosystems.2004.11.003. Epub 2004 Dec 15. Biosystems. 2005. PMID: 15823414

4

Parameter estimation for stiff equations of biosystems using radial basis function networks.

Matsubara Y, Kikuchi S, Sugimoto M, Tomita M. Matsubara Y, et al. Among authors: sugimoto m. BMC Bioinformatics. 2006 Apr 27;7:230. doi: 10.1186/1471-2105-7-230. BMC Bioinformatics. 2006. PMID: 16643665 Free PMC article.

5

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles.

Kitayama T, Kinoshita A, Sugimoto M, Nakayama Y, Tomita M. Kitayama T, et al. Among authors: sugimoto m. Theor Biol Med Model. 2006 Jul 17;3:24. doi: 10.1186/1742-4682-3-24. Theor Biol Med Model. 2006. PMID: 16846504 Free PMC article.

6

HybGFS: a hybrid method for genome-fingerprint scanning.

Shinoda K, Yachie N, Masuda T, Sugiyama N, Sugimoto M, Soga T, Tomita M. Shinoda K, et al. Among authors: sugimoto m. BMC Bioinformatics. 2006 Oct 29;7:479. doi: 10.1186/1471-2105-7-479. BMC Bioinformatics. 2006. PMID: 17069662 Free PMC article.

7

Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome using artificial neural networks.

Shinoda K, Sugimoto M, Yachie N, Sugiyama N, Masuda T, Robert M, Soga T, Tomita M. Shinoda K, et al. Among authors: sugimoto m. J Proteome Res. 2006 Dec;5(12):3312-7. doi: 10.1021/pr0602038. J Proteome Res. 2006. PMID: 17137332

8

Comparison of metabolite production capability indices generated by network analysis methods.

Ishii K, Nakamura S, Morohashi M, Sugimoto M, Ohashi Y, Kikuchi S, Tomita M. Ishii K, et al. Among authors: sugimoto m. Biosystems. 2008 Jan;91(1):166-70. doi: 10.1016/j.biosystems.2007.08.010. Epub 2007 Sep 4. Biosystems. 2008. PMID: 18082935

9

Algebraic method for the analysis of signaling crosstalk.

Matsubara Y, Kikuchi S, Sugimoto M, Oka K, Tomita M. Matsubara Y, et al. Among authors: sugimoto m. Artif Life. 2008 Winter;14(1):81-94. doi: 10.1162/artl.2008.14.1.81. Artif Life. 2008. PMID: 18171132

10

Informatics for peptide retention properties in proteomic LC-MS.

Shinoda K, Sugimoto M, Tomita M, Ishihama Y. Shinoda K, et al. Among authors: sugimoto m. Proteomics. 2008 Feb;8(4):787-98. doi: 10.1002/pmic.200700692. Proteomics. 2008. PMID: 18214845 Review.

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