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. 2023 Aug 5;25(3):29. doi: 10.1007/s10544-023-00671-1

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© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Fig. 4 — Support-vector machines (SVM) training to predict liposome dispersity and stability. (A) The effect of the liposome library size and number of experimental replicates on the model accuracy; (B) The effect of the number of liposome configurations on the model accuracy. (Dots represent mean values and gray areas represent mean ± 1 standard deviation); (C) The effect of the number of experimental replicates per formulation on SVM model accuracy. (Black dots represent the measured accuracy of the SVM model. Gray dots represent the mean model accuracy ± 1 standard deviation following a stratified k-fold cross-validation (k = 10); D. and E. Heatmaps presenting the predictions for the liposome dispersity and liposome stability, post SVM training ( filled diamonds corresponds to the actual experimental values)