Identification of hidden concept modules using correlation analysis of heterogenious data
| Project/Area Number |
26330342
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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 | The University of Tokyo (2017)
Tokyo University of Technology (2014-2016) |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 遺伝子機能 / 遺伝子変異 / 遺伝子発現 / オミックス解析 / バイオマーカー / データ統合 / 細胞内局在 / 遺伝子 / データベース / 多重検定 / タンパク質間相互作用 / 立体構造 / タンパク質機能ドメイン / GSEA / 蛋白質間相互作用 / 蛋白機能ドメイン / 疾患 |
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| Outline of Final Research Achievements |
It is not explicit what relationship exists between the various information (term) in the database. In this research, the purpose is to find a new relationship between information by integrated analysis. First, correlation detection of term information to describe gene function was performed, and it was found that the cell localization information of protein has high correlation with information such as other functions. As a result of investigating the method for enlarging the scale, we applied matrix factorization approach to make it large scale, by which we can skip for computation rigorous correlation factors. At the same time, complex concepts expressed by plural terms can be extracted automatically. When the technique was applied to omics data such as gene expression and mutation, we could find new candidates of cancer biomarkers.
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Report
(5 results)
Research Products
(18 results)
