A study of the Improvement of Reliabilities of Regulations using a Hierarchical Structure in a Genetic Network
Project/Area Number |
26330275
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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 |
Soft computing
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Research Institution | Tottori University |
Principal Investigator |
KIMURA Shuhei 鳥取大学, 工学研究科, 教授 (20342777)
|
Research Collaborator |
OKADA mariko (HATAKEYAMA mariko)
|
Project Period (FY) |
2014-04-01 – 2019-03-31
|
Project Status |
Completed (Fiscal Year 2018)
|
Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
|
Keywords | 遺伝子ネットワーク / 階層性 / ブートストラップ法 / 遺伝子ネットワーク同定 / Genetic network / GENIE3 / 階層構造 / 事前知識 / 疑似焼きなまし法 / 階層構造推定 |
Outline of Final Research Achievements |
Genetic network inference methods generally infer many erroneous regulations. To decrease the number of erroneous regulations, this study uses a priori knowledge that biochemical networks exhibit hierarchical structures. This study detects the hierarchical structure in the target network using a hierarchical random graph model proposed by Clauset and colleagues. When the regulations inferred by the inference method are inconsistent with the detected hierarchical structure, we can conclude that they are unreasonable. However, it is not always easy to detect the hierarchical structure in the target network because of the regulations erroneously inferred by the inference method. To obtain a reasonable hierarchical structure, this study first infers many genetic networks from the observed gene expression data by using a bootstrap-based method. We then extract a hierarchical structure from the inferred multiple genetic networks so that it is consistent with most of the networks.
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Academic Significance and Societal Importance of the Research Achievements |
遺伝子ネットワーク同定は,がんなどの遺伝子を原因とする疾病に対する薬剤の標的遺伝子を見つけるためなどに利用可能と考えられている.本研究では遺伝子ネットワークの同定精度を改善するために,これまでに利用できなかった「ネットワークの階層性」という性質を利用する方法を開発した.提案手法は階層性だけでなく,これまで利用の難しかった複数の知識を利用することが可能と考えられ,さらなる同定精度の改善が期待できる.
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Report
(6 results)
Research Products
(16 results)