| Project/Area Number |
17K00406
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
AMIN MD. ALTAFUL 奈良先端科学技術大学院大学, 先端科学技術研究科, 准教授 (30379531)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Metabolomics / Metabolic Network / Network Clustering / Metabolite Activity / Big Data Biology / Algorithm / Systems Biology / Algorithms / Big data biology / Network clustering / Secondary metabolites |
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| Outline of Final Research Achievements |
We have developed a method to predict the activity of metabolites and have been able to predict the function of 1340 unknown metabolites using this method. As part of the project, we have also developed a tool called DPClusSBO for clustering simple graphs and bipartite graphs. We have published many papers centered around this project and the tool we developed is being used by our collaborators in Malaysia, Brazil and Indonesia. We hope that this tool will be used more widely in the future, and we would like to expand the application of the tool ourselves such as for searching for antibiotics based on traditional medicines.
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| Academic Significance and Societal Importance of the Research Achievements |
By utilizing our method we predicted the functions of 1340 unknown metabolites. As part of this project we developed a tool for clustering of simple and bipartite graphs which we have utilized in several other research works. This tool is a significant academic and social achievement.
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