2004 Fiscal Year Final Research Report Summary
Construction and Retrieval of Highly Integrated Biological Databases
| Project/Area Number |
12208007
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Review Section |
Biological Sciences
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| Research Institution | Kyoto University |
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Principal Investigator |
GOTO Susumu Kyoto University, Institute for Chemical Research, Associate Professor, 化学研究所, 助教授 (40263149)
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| Co-Investigator(Kenkyū-buntansha) |
AKUTSU Tatsuya Kyoto University, Institute for Chemical Research, Professor, 化学研究所, 教授 (90261859)
SATOU Kenji Japan Advanced Institute of Science and Technology, School of Knowledge Science, Associate Professor, 知識科学研究科, 助教授 (10215783)
HATTORI Masahiro Kyoto University, Institute for Chemical Research, Instructor, 化学研究所, 助手 (60372554)
OKUNO Yasushi Kyoto University, Institute for Chemical Research, Instructor, 化学研究所, 助手 (20283666)
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| Project Period (FY) |
2000 – 2004
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| Keywords | Database / Bioinformatics / Ontology / Algorithm / Molecular interaction / Reaction network / GRID / Network topology |
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| Research Abstract |
We have constructed a database of molecular interactions and developed methods for extracting novel biological knowledge from it. It is important for such a database to be able to handle chemical information as well as genomic and proteomic information as an integrated manner. Considering this viewpoint, we have achieved the following three main results.
1.BRITE database
We have developed the BRITE database storing direct and indirect molecular interaction data as binary relations. It mainly consists of protein interaction data from yeast two-hybrid systems, neighboring enzyme relations in the KEGG metabolic pathway, and relationship between transcription factors and their target genes in the KEGG regulatory pathway. BRITE has a facility to retrieve these binary data and display them as a network.
2.Ontology extraction from genome databases
We implemented a general framework for extracting relationships among data. Using the association rule discovery method that is one of the well-known d
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ata mining methods, it quickly discovers common and specific features to a given set of entries. Next we defined relationship among keywords and entries by constructing a huge dictionary derived from genome databases. We also constructed a GRID environment for developing huge databases.
3.Integration of chemical information into the database and analysis of network topologies
We developed a representation format for secondary structures of chemical compounds in terms of reactivity and a method for comparing chemical structures based on the format. We also developed an algorithm to infer rules of chemical structure conversion in the enzyme reactions, and construct a database of reactant pairs by applying it. This database was further applied to a prediction of novel enzyme reaction pathways. Regarding the topology analysis, two networks created by the pairs of chemical compounds and enzyme relations were our targets. We obtained new insights into the relationship between the two networks and functional modules in the metabolic network. Less
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