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1 article(s) from Das, Totan

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Indrasis Dasgupta,
Totan Das,
Biplab Das and
Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

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Figure 1: Workflow of the current study for cellular uptake of ENMOs involving different approaches such as B...

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Figure 2: Receiver operating characteristic plots of the training set (A, C, E) and test set (B, D, F) for th...

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Figure 3: Uptake-promoting (UP_p 1–UP_p 20) and uptake-impairing (UI_p 1–UI_p 20) fingerprints from the Bayesian ...

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Figure 4: Uptake-promoting (UP_h 1–UP_h 20) and uptake-impairing (UI_h 1–UI_h 20) fingerprints from the Bayesian ...

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Figure 5: Uptake-promoting (UP_u 1–UP_u 20) and uptake-impairing (UI_u 1–UI_u 20) fingerprints from the Bayesian ...

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Figure 6: Receiver operating characteristic plots of training set (A, C, E) and test set (B, D, F) for the ML...

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Figure 7: SHAP summary plot for the ML-based RFC model (training set) in the case of PaCa2 cell line.

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Figure 8: SHAP summary plot for the ML-based SVC model (training set) for the HUVEC cell line.

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Figure 9: SHAP summary plot for the ML-based LDA model (training set) in the case of the U937 cell line.

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Figure 10: Summary of structural features of surface modifiers of ENMOs for the uptake in the PaCa2, HUVEC, an...

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Published 22 Jul 2024

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