2019 Fiscal Year Final Research Report
A big data approach to function prediction of metabolites by clustering of structural similarity networks
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 |
Section | 一般 |
Research Field |
Life / Health / Medical informatics
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Research Institution | Nara Institute of Science and Technology |
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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Keywords | Metabolomics / Metabolic Network / Network Clustering / Metabolite Activity / Big Data Biology / Algorithm |
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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Free Research Field |
Systems Biology
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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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