2018 Fiscal Year Research-status 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 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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| Keywords | Metabolomics / Metabolic Network / Metabolite Activity / Systems Biology / Network Clustering / Big data biology / Network clustering / Algorithms |
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| Outline of Annual Research Achievements |
Under this project, we collected metabolite activity data and structural information of metabolites from KNApSAcK database and constructed the structural similarity based network of metabolites. Also we developed a network clustering tool called DPClusOST which we applied for function prediction of metabolites. It has been proposed that structural similarity between metabolites implies functional similarity between them. In light of this fact we propose a method for function prediction of secondary metabolites based on association philosophy. We are writing a manuscript on this now and will publish by this year. In parallel we have done several other works. Based on the research related to this project, we got five publications during last one year and submitted a paper to IEEE TRANSACTIONS ON Computational Biology and Bioinformatics, which is now under review.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We developed an algorithm and tool for our project.We performed some extra work on the algorithm by extending it for biclustering and also used it in other research and published the following papers:
1. Mohammand Bozlul Karim ; Shigehiko Kanaya;Md. Altaf-Ul-Amin; “Comparison of BiClusO with five different biclustering algorithms using biological and Synthetic data”, Proceedings of the 7th International Conference on Complex Networks and Their Applications; December 11-13, 2018, Cambridge, United Kingdom.
2. 1.Eguchi, R., Karim, M. B., Hu, P., Sato, T., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M. (2018). An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease. BMC bioinformatics, 19(1), 264.
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| Strategy for Future Research Activity |
This year we are conducting some more experiments on prediction accuracy assessment. There is possibility that certain activity categories are similar and hence metabolites belonging to those categories are also structurally similar. We will utilize our results to determine function-function relations. Summarizing all findings, we are preparing the manuscript of a journal paper titled as follows: “Function prediction of metabolites by repetitive clustering of the structural similarity based networks”. We hope to submit it within next 3 months.
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[Journal Article] An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease2018
Author(s)
Eguchi, R., Karim, M. B., Hu, P., Sato, T., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M.
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Journal Title
BMC bioinformatics
Volume: 19(1)
Pages: 264
DOI
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[Journal Article] Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks2018
Author(s)
3.Antonio, V. A. A., Ono, N., Saito, A., Sato, T., Altaf-Ul-Amin, M., & Kanaya, S.
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Journal Title
International journal of computer assisted radiology and surgery
Volume: 13(12)
Pages: 1905-1913
DOI
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