Work

Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching

Year: 2018

Type: conference-paper

Abstract: Cellular Electron CryoTomography (CECT) is a 3D imaging technique that captures information about the structure and spatial organization of macromolecular complexes within single cells, in near-native state and at sub-molecular resolution. Although template matching is often used to locate macromolecules in a CECT image, it is insufficient as it only measures the relative structural similarity. Therefore, it is preferable to assess the statistical credibility of the decision through hypothesis testing, requiring many templates derived from a diverse population of macromolecular structures. Due to the very limited number of known structures, we need a generative model to efficiently and reliably sample pseudo-structures from the complex distribution of macromolecular structures. To address this challenge, we propose a novel image-derived approach for performing hypothesis testing for template matching by constructing generative models using the generative adversarial network (GAN). We conducted hypothesis testing experiments for template matching on both simulated and experimental subtomograms, allowing us to conclude the identity of subtomograms with high statistical credibility and significantly reducing false positives. Our general approach can be extended to assess other template matching tasks, such as face, iris, and fingerprint scans, where statistical tests are also very important.

Source: PubMed

DOI: 10.48550/arxiv.1805.04634

Authors: Wang, Kai Wen, Zeng, Xiangrui, Liang, Xiaodan, Huo, Zhiguang, Xing, Eric P., Xu, Min

Institutions: Carnegie Mellon University, University of Florida

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Pennsylvania Department of Health, National Institutes of Health, National Institute on Drug Abuse, National Institute of General Medical Sciences

Awards: Toward PanOmic and Personalized Association Study of Complex Diseases - A New Statistical and Computational Paradigm for Personalized Medicine, NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR), High Performance Computing for Multiscale Modeling of Biological Systems, G9000039968