Machine Learning for Cryo-EM

Machine Learning for Cryo-EM

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. Our current 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.