TY - GEN
T1 - Forecasting Model for the Annual Growth of Cryogenic Electron Microscopy Data
AU - Abu Al-Haija, Qasem
AU - Al Nasr, Kamal
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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 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.
AB - 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 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.
KW - 3DEM
KW - Auto-regressive modeling
KW - Auto-regressive prediction
KW - Electron Microscopy
KW - NMR
KW - Protein structure
KW - Single particle
KW - Tomography
KW - X-ray crystallography
UR - http://www.scopus.com/inward/record.url?scp=85090004556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090004556&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-46165-2_12
DO - 10.1007/978-3-030-46165-2_12
M3 - Conference contribution
AN - SCOPUS:85090004556
SN - 9783030461645
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 158
BT - Computational Advances in Bio and Medical Sciences - 9th International Conference, ICCABS 2019, Revised Selected Papers
A2 - Mandoiu, Ion
A2 - Rajasekaran, Sanguthevar
A2 - Murali, T.M.
A2 - Narasimhan, Giri
A2 - Skums, Pavel
A2 - Zelikovsky, Alexander
PB - Springer
T2 - 9th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2019
Y2 - 15 November 2019 through 17 November 2019
ER -