Machine Learning and Artificial Intelligence
Single-particle analysis by cryo-electron microscopy (cryo-EM) is evolving into a powerful generic approach in structure determination. However, the noisy nature of cryoelectron micrographs hinders the maximal extraction of various structural information recorded in the micrographs. Conventional data analysis for single-particle cryo-EM has largely benefited from applications of multivariate data analysis approaches, such as principal component analysis (PCA), K-means clustering and linear regression. Further applications of statistical approaches such as maximum likelihood estimation and Bayesian theorem have led to improved single-particle reconstructions and reduced subjectivity in structure refinement. However, as a matter of fact, those advanced algorithms and statistical approaches developed in the areas of machine learning and artificial intelligence in computer science over last several decades have not yet been adapted to cryo-EM data analysis. There are great chances that the adaptation, evolution and innovation of cutting-edge machine learning approaches shall release great potential of single-molecule cryo-EM approaches and expand the applicability of this technology to meet the future challenges in life sciences and medicine. The current Intel® PCCSB research will attempt to innovate the cutting-edge approaches in machine learning and artificial intelligence for cryo-EM data analysis to address the future challenges in structural biology discovery. We are particularly interested in developing these approaches in the studies of biomolecular complex dynamics and structural systems biology.
J. Wu, Y. Ma, C. Congdon, B. Brett, S. Chen, Q. Ouyang, Y. Mao. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. PLoS ONE 12, e0182130 (2017). https://doi.org/10.1371/ journal.pone.0182130. arXiv: 1604.04539. Read
Y. Zhu, Q. Ouyang, Y. Mao. A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy. BMC Bioinformatics 18, 348 (2017). Read
Y. Xu, J. Wu, C.C. Yin, Y. Mao. Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm. PLoS ONE 11, e0167765 (2016). doi: 10.1371/journal.pone.0167765. arXiv: 1609.02213 [q-bio.QM]. Read
ROME: A machine-learning based HPC software package for cryo-EM data processing. Learn More
DeepEM: A deep-learning based particle recognition program. Learn More
ACK-means: Adaptively constrained K-means algorithm for data clustering. Learn More