Project/Area Number |
23K05660
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 43020:Structural biochemistry-related
|
Research Institution | Tokyo Metropolitan University |
Principal Investigator |
PETER GUENTERT 東京都立大学, 理学研究科, 客員教授 (20392110)
|
Co-Investigator(Kenkyū-buntansha) |
池谷 鉄兵 東京都立大学, 理学研究科, 准教授 (30457840)
伊藤 隆 東京都立大学, 理学研究科, 教授 (80261147)
|
Project Period (FY) |
2023-04-01 – 2026-03-31
|
Project Status |
Granted (Fiscal Year 2023)
|
Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2025: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2024: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2023: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
Keywords | NMR / protein / machine learning / spectra analysis / ARTINA |
Outline of Research at the Start |
Using generated training data, we will apply deep learning for (i) a better treatment of time-domain data acquired with non-uniform sampling (NUS) than conventional approaches, (ii) resolution enhancement by virtual decoupling, and (iii) deconvolution of highly overlapped signals. In this way, time-consuming or costly experimental methods for obtaining better NMR spectra can be replaced by computational alternatives, thereby increasing the efficiency, completeness, and quality of NMR spectra analysis.
|