2002 Fiscal Year Final Research Report Summary
Foundations of Computational Knowledge Discovery from cDNA Microarray Data
| Project/Area Number |
12480080
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MIYANO Satoru The University of Tokyo, Institute of Medical Science, Professor, 医科学研究所, 教授 (50128104)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUNO Hiroshi Yamaguchi University, Faculty of Science, Associate Professor, 理学部, 助教授 (10181744)
AKUTSU Tatsuya Kyoto University, Institute for Chemical Research, Professor, 化学研究所, 教授 (90261859)
KUHARA Satoru Kyushu University, Graduate School of Genetic Resources Technology, Professor, 農学研究院, 教授 (00153320)
MARUYAMA Osamu Kyushu University, School of Mathematics, Associate Professor, 数理学研究院, 助教授 (20282519)
SHINOHARA Ayumi Kyushu University, Department of Informatics, Associate Professor, システム情報科学研究院, 助教授 (00226151)
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| Project Period (FY) |
2000 – 2002
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| Keywords | Gene network / Knowledge discovery / Qualitative network / Boolean network / Microarray / Hybrid Petri net / Systems biology / Computational learning |
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| Research Abstract |
Since the beginning of this research project, the cDNA microarrays have been intensively employed for measuring gene expressions in laboratories. Some projects on genome-wide gene expression analysis have planed where commercialized statistical analysis program packages and databases are employed as bioinformatics tools in an ad hoc way. However, foundations based on information science were not paid attentions so much while only software tools have been developed from the viewpoint of practice in Japan.
This reseach project has contributed to the development of novel microarray data analysis technology. First, we have created a mathematical framework for gene network models from cDNA microarray data (Boolean network model, Bayesian network model, system of ordinary differential equations), then we have developed computational methods for inferring these models from cDNA microarray data. These methods were examined through real biological analyzes. The second contribution is a development of modeling and simulation technology for gene networks (an extention of hybrid Petri net and model simulation of various biopathways).
With these research contributions, we have established a computational strategy for developing systems biology.
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[Publications] Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.: "Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network"J.Bioinformatics and Computational Biology. (in press). (2003)
Description
「研究成果報告書概要(和文)」より
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[Publications] Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara,S., Miyano, S.: "Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network"J. Bioinformatics and Computational Biology. in press. (2003)
Description
「研究成果報告書概要(欧文)」より
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