Fig. 5: On-chip 3D assay exhibits improved prediction power for clinical outcomes. | Nature Communications

Fig. 5: On-chip 3D assay exhibits improved prediction power for clinical outcomes.

From: On-chip 3D potency assay for prediction of clinical outcomes for cell therapy candidates for osteoarthritis

Fig. 5

A LASSO model built from iterative learning of a logistic regression model with 24 secreted protein concentrations as the dependent variable and clinical outcomes as the independent variable. Outcomes are represented as responding and non-responding, or Yes (Y) and No (N), respectively. There are 4 k-folds, each consisting of a training fold (n = 15) and a validation fold (n = 4). Each validation fold has one unique non-responding (N) sample. To address this imbalance between responders and non-responders, we use balanced accuracy rather than the usual accuracy as a measure of performance. B Cross-validation accuracy of on-chip 3D ctrl samples is greater than on-chip 3D simSF and 2D simSF. Cross-validation accuracy results comprise an average of 100 repeats of the prediction model for each fold (400 points per platform). ANOVA performed across platforms with two-sided t-tests for post-hoc tests. Error represented as SEM. C The top three contributing analytes to each model with listed model weights.

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