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Cryopreservation is beset with the challenge of protocol alignment across awide range of cell types and process variables. By taking a cross-sectionalassessment of previously published cryopreservation data (sample means andstandard errors) as preliminary meta-data, a decision tree learning analysis(DTLA) was performed to develop an understanding of target survival andoptimized pruning methods based on different approaches. Briefly, a cleardirection on the decision process for selection of methods was developed withkey choices being the cooling rate, plunge temperature on the one hand andbiomaterial choice, use of composites (sugars and proteins), loading procedureand cell location in 3D scaffold on the other. Secondly, using machine learningand generalized approaches via the Na\"ive Bayes Classification (NBC) approach,these metadata were used to develop posterior probabilities for combinatorialapproaches that were implicitly recorded in the metadata. These latter resultsshowed that newer protocol choices developed using probability elicitationtechniques can unearth improved protocols consistent with multipleunidimensional optimized physical protocols. In conclusion, this articleproposes the use of DTLA models and subsequently NBC for the improvement ofmodern cryopreservation techniques through an integrative approach.  Keywords: 3D cryopreservation, decision-tree learning (DTL), sugars, mouseembryonic stem cells, meta-data, Na\"ive Bayes Classifier (NBC)

A Bayesian Approach to Optimizing Stem Cell Cryopreservation protocols