Article

Forecasting Model for the Annual Growth of Cryogenic Electron Microscopy Data

Authors:

Qasem Abu Al-Haija,

Kamal Al NasrAuthors Info & Claims

Computational Advances in Bio and Medical Sciences: 9th International Conference, ICCABS 2019, Miami, FL, USA, November 15–17, 2019, Revised Selected Papers

Pages 147 - 158

https://doi.org/10.1007/978-3-030-46165-2_12

Published: 15 November 2019 Publication History

Abstract

In this paper, we develop a forecasting model for the growth of Cryogenic Electron Microscopy (Cryo-EM) experimental data time series using autoregressive (AR) model. We employ the optimal modeling order that maximizes the estimation accuracy while maintaining the least normalized prediction error. The proposed model has been efficiently used to forecast the growth of cryo-EM data for the next 10 years, 2019–2028. The time series for the number of released three-dimensional Electron Microscopy (3DEM) images along with the time series of the annual number of 3DEM achieving resolution 10 Å or better are used. The data was collected from the public Electron Microscopy Data Bank (EMDB). The simulation results showed that the optimal model orders to estimate both datasets are and respectively. Consequently, the optimal models obtained an estimation accuracy of and for 3DEM experiments time series and 3DEM resolutions time series, respectively. Hence, the forecasting results reveal an exponential increasing behavior in the future growth of annual released of 3DEM and, similarly, for the annual number of 3DEM achieving resolution 10 Å or better.

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Cited By

Chen LJebril RAl Nasr K(2020)Segmentation-based Feature Extraction for Cryo-Electron Microscopy at Medium ResolutionProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414711(1-9)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414711
Jebril RZhu YChen WAl Nasr K(2020)A Divide and Conquer Algorithm for Electron Microscopy SegmentationProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414700(1-7)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414700

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Forecasting Model for the Annual Growth of Cryogenic Electron Microscopy Data

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Published In

cover image Guide Proceedings

Computational Advances in Bio and Medical Sciences: 9th International Conference, ICCABS 2019, Miami, FL, USA, November 15–17, 2019, Revised Selected Papers

Nov 2019

209 pages

ISBN:978-3-030-46164-5

DOI:10.1007/978-3-030-46165-2

Editors:
Ion Măndoiu
University of Connecticut, Storrs, CT, USA
,
T. M. Murali
Virginia Tech, Blacksburg, VA, USA
,
Giri Narasimhan
Florida International University, Miami, FL, USA
,
Sanguthevar Rajasekaran
University of Connecticut, Storrs, CT, USA
,
Pavel Skums
Georgia State University, Atlanta, GA, USA
,
Alexander Zelikovsky
Georgia State University, Atlanta, GA, USA

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 November 2019

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Cited By

Chen LJebril RAl Nasr K(2020)Segmentation-based Feature Extraction for Cryo-Electron Microscopy at Medium ResolutionProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414711(1-9)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414711
Jebril RZhu YChen WAl Nasr K(2020)A Divide and Conquer Algorithm for Electron Microscopy SegmentationProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414700(1-7)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414700

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