Min Xu receives first NSF grant
Assistant Research Professor Min Xu recently received notice that he has been awarded an NSF grant for developing novel computational methods to significantly reduce the amount of annotations required for supervised deep learning based cryo-electron tomography analysis.
Cells are basic structural and functional units of all known living organisms. Understanding the structures of large individual macromolecules and their complexes inside cells is fundamental to biological research community. However, such structural information has been extremely difficult to obtain due to the lack of data acquisition techniques. Recent advances in cellular electron cryo-tomography have enabled 3D imaging of cellular structure and organization at sub-molecular resolution and in near-native state. The resulting images contain tens of millions of structurally highly diverse macromolecules, which introduces a major challenge in the throughput of subsequent computational analysis: how to efficiently and accurately process the images to identify each distinct type of macromolecular complex. The project will focus on reducing data annotation costs using supervised deep learning. The algorithms and software developed in the project will have direct benefit to the structural and cell biology communities. To facilitate broad use of the methods developed from the project, they will be incorporated into open-source CECT analysis package, AITom.