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我的研究及感兴趣的领域包括以下几个方面:生物信息 (Bioinformatics)、医学图像处理 (Medical imaging processing)、机器学习 (Machine learning)、高性能计算 (High performance computing)、逆问题求解 (Inverse problem)。我致力于从真实的生物或医学问题中抽像出数学与计算模型,并利用机器学习、高性能计算等手段开发高速有效的算法以进行求解。我目前实际开展的研究包括 电镜图像处理及其三维重构 (Cryo-EM, Single particle analysis, Electron tomography),超分辨图像重建及其在光学显微镜中的应用 (Super-resolution image reconstruction, Super-resolution fluorescence microscopy),基于深度学习的图像分割识别及重构恢复的研究 (Deep learning guided image segmentation/classification/reconstruction),针对三代测序数据的算法开发 (Algorithms for third-generation sequencing) 等等。
- 宋鑫涛. Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information. NATURE COMMUNICATIONS, 15, 2024.
- . TDFPS-Designer: an efficient toolkit for barcode design and selection in nanopore sequencing. GENOME BIOLOGY, 25, 2024.
- . vEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy. GIGASCIENCE, 13, 2024.
- . Markerauto2: A fast and robust fully automatic fiducial marker-based tilt series alignment software for electron tomography. STRUCTURE, 2024.
- Qi, Junhai. FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval. Genomics, proteomics & bioinformatics, 22, 2024.
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