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On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments
May-June 2012 (vol. 9 no. 3)
pp. 846-856
ASCII Text
x 
 
F. Barry, M. J. Murphy, D. Fay, M. Norkus, G. Olaighin, L. Kilmartin, "On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 846-856, May-June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2011.155,
author = {F. Barry and M. J. Murphy and D. Fay and M. Norkus and G. Olaighin and L. Kilmartin},
title = {On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {3},
issn = {1545-5963},
year = {2012},
pages = {846-856},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.155},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments
IS - 3
SN - 1545-5963
SP846
EP856
EPD - 846-856
A1 - F. Barry,
A1 - M. J. Murphy,
A1 - D. Fay,
A1 - M. Norkus,
A1 - G. Olaighin,
A1 - L. Kilmartin,
PY - 2012
KW - physiological models
KW - biomembranes
KW - bone
KW - cellular biophysics
KW - Gaussian processes
KW - learning (artificial intelligence)
KW - medical computing
KW - orthopaedics
KW - temperature -40 degC to 20 degC
KW - Gaussian process model
KW - active learning
KW - postcryopreservation cell membrane integrity experiments
KW - biological cell cryopreservation
KW - stem cell cryostorage
KW - regenerative medicine
KW - optimal cryopreservation processing
KW - elevated temperature conditions
KW - mesenchymal Stem Cells
KW - human bone marrow
KW - imminent detrimental effect
KW - cell integrity
KW - Temperature distribution
KW - Kernel
KW - Data models
KW - Biomembranes
KW - Vectors
KW - Stem cells
KW - temperature elevation.
KW - Active learning
KW - Gaussian process
KW - postcryopreservation cell integrity
KW - stem cells
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
F. Barry, Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
M. J. Murphy, Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
D. Fay, Comput. Lab., Univ. of Cambridge, Cambridge, UK
M. Norkus, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
G. Olaighin, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
L. Kilmartin, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
Biological cell cryopreservation permits storage of specimens for future use. Stem cell cryostorage in particular is fast becoming a broadly spread practice due to their potential for use in regenerative medicine. For the optimal cryopreservation process, ultralow temperatures are needed. However, elevated temperatures are often unavoidable in a typical sample handling cycle which in turn negatively affects the postcryopreservation integrity of cells. In this paper, we present an application of active learning using an underlying Gaussian Process (GP) model in an experimental study on postcryopreservation membrane integrity response to a range of elevated temperature conditions. We tailored this technique for the current investigation and developed an algorithm which enabled identification of the sampling locations for the experiments in order to obtain the highest information return about the process from a limited size sample set. We applied this algorithm in the experimental study investigating the effects of severe temperature elevation (ranging from -40 to 20°C) over a short term event (48 hours) on the postcryopreservation membrane integrity of Mesenchymal Stem Cells (MSCs) derived from human bone marrow. The algorithm showed excellent performance by selecting the locations which maximized the reduction of variance of the process response estimate. An approximating GP model developed from this experimental data shows that the elevated temperatures during cryopreservation have an imminent detrimental effect on cell integrity.

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Index Terms:
physiological models,biomembranes,bone,cellular biophysics,Gaussian processes,learning (artificial intelligence),medical computing,orthopaedics,temperature -40 degC to 20 degC,Gaussian process model,active learning,postcryopreservation cell membrane integrity experiments,biological cell cryopreservation,stem cell cryostorage,regenerative medicine,optimal cryopreservation processing,elevated temperature conditions,mesenchymal Stem Cells,human bone marrow,imminent detrimental effect,cell integrity,Temperature distribution,Kernel,Data models,Biomembranes,Vectors,Stem cells,temperature elevation.,Active learning,Gaussian process,postcryopreservation cell integrity,stem cells
Citation:
F. Barry, M. J. Murphy, D. Fay, M. Norkus, G. Olaighin, L. Kilmartin, "On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 846-856, May-June 2012, doi:10.1109/TCBB.2011.155
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