2020Lai Statistics: Difference between revisions
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== Citation == | == Citation == | ||
Lai, T. L.; Wang, S.-H.; Yao, Y.-C.; Chung, S.-C.; Chang, W.-H.; Tu, I.-P. Cryo-EM: breakthroughs in chemistry, structural biology, and statistical underpinnings, Stanford Univ., | Lai, T. L.; Wang, S.-H.; Yao, Y.-C.; Chung, S.-C.; Chang, W.-H.; Tu, I.-P. Cryo-EM: breakthroughs in chemistry, structural biology, and statistical underpinnings, Stanford Univ., TechReport Dept. Statistics 2020-14, 2020 | ||
== Abstract == | == Abstract == | ||
Latest revision as of 08:25, 3 February 2021
Citation
Lai, T. L.; Wang, S.-H.; Yao, Y.-C.; Chung, S.-C.; Chang, W.-H.; Tu, I.-P. Cryo-EM: breakthroughs in chemistry, structural biology, and statistical underpinnings, Stanford Univ., TechReport Dept. Statistics 2020-14, 2020
Abstract
Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, organic and medicinal chemistry, molecular and cellular physiology, and its fundamental importance was recognized in the 2017 Nobel Prize in Chemistry. Herein we �first review the statistical underpinnings of high-resolution 3D image reconstruction from 2D cryo-EM data that have three characteristic features: missing data, high noise level, and massive datasets. We then discuss challenges and opportunities for statistical science posed by high-resolution structure determination using cryo-EM, and review recent advances in high-dimensional multivariate analysis, dimension reduction, maximum a posteriori estimation of latent variables, hidden Markov models and uncertainty quanti�cation in this connection.
Keywords
Links
https://statistics.stanford.edu/sites/g/files/sbiybj6031/f/2020-14.pdf