Project/Area Number |
23K23818
|
Research Category |
Grant-in-Aid for Scientific Research (B)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 43020:Structural biochemistry-related
|
Research Institution | The University of Tokyo |
Principal Investigator |
Danev Radostin 東京大学, 大学院医学系研究科(医学部), 教授 (50415931)
|
Project Period (FY) |
2024-04-01 – 2025-03-31
|
Project Status |
Granted (Fiscal Year 2024)
|
Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2024: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
|
Keywords | cryo-EM / automation / sample screening / data acquisition / deep learning |
Outline of Research at the Start |
Cryo-electron microscopy (cryo-EM) has become an essential tool for studying the atomic structure of proteins. However, the technique remains quite challenging for inexperienced users. In this project, we will develop deep learning-based AI-automation methods that will transform the electron microscope into an intelligent imaging system and will help researchers to achieve high quality results faster, and with less struggle. We will apply the new machine learning methods to single particle analysis and cryo-tomography studies of membrane proteins and cell structures.
|