Fast Similarity Search on Big Data based on SMAD and its applications
Project/Area Number |
25280002
|
Research Category |
Grant-in-Aid for Scientific Research (B)
|
Allocation Type | Partial Multi-year Fund |
Section | 一般 |
Research Field |
Theory of informatics
|
Research Institution | The University of Tokyo |
Principal Investigator |
Shibuya Tetsuo 東京大学, 医科学研究所, 准教授 (60396893)
|
Project Period (FY) |
2013-04-01 – 2017-03-31
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥10,400,000 (Direct Cost: ¥8,000,000、Indirect Cost: ¥2,400,000)
Fiscal Year 2016: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2015: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2013: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
Keywords | アルゴリズム / ビッグデータ / 検索 / タンパク質立体構造 / 次世代シークエンサー / バイオインフォマティクス / データ検索 / 機械学習 |
Outline of Final Research Achievements |
We aimed to develop very fast indexing and searching algorithms for big-data databases, especially the protein 3-D structure databases, and also aimed to develop application algorithms utilizing them. We succeeded in developing dramatically faster protein function prediction algorithms without any loss of accuracy. We also succeeded in developing faster algorithms for protein 3-D structure searching for wider applications. We also developed several analysis algorithms for next-generation sequencer data.
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Report
(5 results)
Research Products
(22 results)