Development of statistical methods for large scale somatic mutation data mining
| Project/Area Number |
15K00398
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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 |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | がんゲノム / 機械学習 / パターンマイニング / 可視化 / 統計手法 / 変異シグナチャー / スプライシング / 統計モデル / トピックモデル / 最適化 |
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| Outline of Final Research Achievements |
We have developed a novel statistical method for extracting characteristic pattern from somatic mutation data (Shiraishi et al., 2015, https://github.com/friend1ws/pmsignature). Assuming the independence on each factor of mutation signatures and reducing the number of parameters, more robust and interpretable estimates can be obtained. Additionally, the proposed model has close relationships with the “mixed-membership models,” that have been intensively utilized in statistical machine learning and statistical genetics community. Furthermore, we have applied this approach to the set of splicing associated variants and identified several novel patterns (Shiraishi et al., BioRxiv, 2017).
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Report
(4 results)
Research Products
(19 results)
