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International Conference on Computational Advances in Bio and Medical Sciences

ICCABS 2019: Computational Advances in Bio and Medical Sciences pp 147–158Cite as

Forecasting Model for the Annual Growth of Cryogenic Electron Microscopy Data

Part of the Lecture Notes in Computer Science book series (LNBI,volume 12029)

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 \( AR\left( 5 \right) \) and \( AR\left( 6 \right) \) respectively. Consequently, the optimal models obtained an estimation accuracy of \( 96.8\%, \) and \( 85\% \) 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.

Keywords

  • Protein structure
  • Electron Microscopy
  • 3DEM
  • Single particle
  • Tomography
  • X-ray crystallography
  • NMR
  • Auto-regressive modeling
  • Auto-regressive prediction

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Acknowledgments

This work was supported by the US National Science Foundation (NSF) Research Initiation Award (RIA) (HRD: 1600919) and the NIH Research grant (R15-AREA: 1R15GM126509-01).

Author information

Authors and Affiliations

  1. Department of Computer and Information Systems Engineering (CISE), Tennessee State University, Nashville, TN, USA

    Qasem Abu Al-Haija

  2. Department of Computer Science, Tennessee State University, Nashville, TN, USA

    Kamal Al Nasr

Authors
  1. Qasem Abu Al-Haija
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  2. Kamal Al Nasr
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Corresponding author

Correspondence to Kamal Al Nasr .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Prof. Ion Măndoiu

  2. Virginia Tech, Blacksburg, VA, USA

    T. M. Murali

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. University of Connecticut, Storrs, CT, USA

    Sanguthevar Rajasekaran

  5. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  6. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Abu Al-Haija, Q., Al Nasr, K. (2020). Forecasting Model for the Annual Growth of Cryogenic Electron Microscopy Data. In: Măndoiu, I., Murali, T., Narasimhan, G., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2019. Lecture Notes in Computer Science(), vol 12029. Springer, Cham. https://doi.org/10.1007/978-3-030-46165-2_12

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

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-46165-2

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