Modeling Uptake of Polyethylenimine/Short Interfering RNA Nanoparticles in Breast Cancer Cells Using Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
Polyethylenimine (PEI) is one of the most promising nonviral vectors for delivery of short interfering RNA (siRNA) agents into cancer cells. A promising approach that increases the delivery efficiency of PEI is its modification with hydrophobic substitutions. However, the performance of modified PEIs depends on the nature and extent of substitutions. Herein, machine learning algorithms are used on the basis of quantitative structure activity relationship (QSAR) method to predict the cellular uptake of hydrophobically modified PEI/siRNA nanoparticles (NPs) into various cancer cell lines. To this end, 3 different regression models, namely, random forest (RF), multilayer perceptron (MLP), and linear regression (LR), are used. The results show that RF and MLP regression methods have a better performance than the LR method, suggesting that nonlinear models are better estimators when predicting the cellular uptake of PEI/siRNA NPs. Additionally, critical descriptors that have major contributions to cellular uptake are found to be PEI‐to‐siRNA weight ratio, type of hydrophobic substitution, as well as total numbers of Cs, unsaturated C, and thioester groups on substitutions in each PEI. This study is the first report that predicts cellular uptake with PEI‐based carriers, which provides valuable insight into the design of performance‐enhancing hydrophobic substituents on PEIs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it