research-article

Using Curriculum Learning in Pattern Recognition of 3-dimensional Cryo-electron Microscopy Density Maps

Authors:

Jing HeAuthors Info & Claims

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Article No.: 112, Pages 1 - 7

https://doi.org/10.1145/3388440.3414710

Published: 24 November 2020 Publication History

Abstract

Although Cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structure when the resolution of cryo-EM density maps is in the medium range, e.g., 5-10 Å. Studies have attempted to utilize machine learning methods, especially deep neural networks to build predictive models for the detection of protein secondary structures from cryo-EM images, which ultimately helps to derive the atomic structure of proteins. However, the large variation in data quality makes it challenging to train a deep neural network with high prediction accuracy. Curriculum learning has been shown as an effective learning paradigm in machine learning. In this paper, we present a study using curriculum learning as a more effective way to utilize cryo-EM density maps with varying quality. We investigated three distinct training curricula that differ in whether/how images used for training in past are reused while the network was continually trained using new images. A total of 1,382 3-dimensional cryo-EM images were extracted from density maps of Electron Microscopy Data Bank in our study. Our results indicate learning with curriculum significantly improves the performance of the final trained network when the forgetting problem is properly addressed.

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Cited By

Cao HHe JLi THuang S(2025)Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep LearningJournal of Chemical Information and Modeling10.1021/acs.jcim.4c0197165:3(1641-1652)Online publication date: 22-Jan-2025
https://doi.org/10.1021/acs.jcim.4c01971
Sazzed S(2024)Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESegACS Omega10.1021/acsomega.4c026089:24(26409-26416)Online publication date: 8-Jun-2024
https://doi.org/10.1021/acsomega.4c02608
Nguyen TMu YSun JHe JWang MYoon B(2023)An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density MapsProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612947(1-8)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612947

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cover image ACM Conferences

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2020

193 pages

ISBN:9781450379649

DOI:10.1145/3388440

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Published: 24 November 2020

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BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 21 - 24, 2020

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Cited By

Cao HHe JLi THuang S(2025)Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep LearningJournal of Chemical Information and Modeling10.1021/acs.jcim.4c0197165:3(1641-1652)Online publication date: 22-Jan-2025
https://doi.org/10.1021/acs.jcim.4c01971
Sazzed S(2024)Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESegACS Omega10.1021/acsomega.4c026089:24(26409-26416)Online publication date: 8-Jun-2024
https://doi.org/10.1021/acsomega.4c02608
Nguyen TMu YSun JHe JWang MYoon B(2023)An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density MapsProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612947(1-8)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612947

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