Information-based objective functions for active data selection
Neural Computation
Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Gaussian Process Regression: Active Data Selection and Test Point Rejection
Mustererkennung 2000, 22. DAGM-Symposium
Bayesian treed gaussian process models
Bayesian treed gaussian process models
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
The Journal of Machine Learning Research
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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^{\circ }{\rm 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.