# SHAP

P-glycoprotein efflux limits small-molecule bioavailability and central-nervous-system exposure. SF-GA embeds fragment synergy analysis in the mutation step of a genetic algorithm, preferentially swapping in substructures that co-occur with the non-substrate class, so predicted P-gp substrates can be rewritten into predicted non-substrates while the scaffold is retained. On the PMO benchmark under a fixed function-evaluation budget, SF-GA's optimization performance is comparable to GB-GA, REINVENT and FragGT, and it reaches the highest mean RAscore (0.932) among all methods tested. The molecules remain hypothetical computational predictions, and non-substrate behavior has not been experimentally validated.

P-glycoprotein efflux limits small-molecule bioavailability and central-nervous-system exposure. SF-GA embeds fragment synergy analysis in the mutation step of a genetic algorithm, preferentially swapping in substructures that co-occur with the non-substrate class, so predicted P-gp substrates can be rewritten into predicted non-substrates while the scaffold is retained. On the PMO benchmark under a fixed function-evaluation budget, SF-GA's optimization performance is comparable to GB-GA, REINVENT and FragGT, and it reaches the highest mean RAscore (0.932) among all methods tested. The molecules remain hypothetical computational predictions, and non-substrate behavior has not been experimentally validated.

scCont breaks cell-state transitions in time-course single-cell RNA data into interpretable functional transition units: instead of explicit time labels or clustering priors, it selects positive pairs with a k-nearest-neighbor graph in expression space for contrastive learning, then uses Shapley attribution to map latent dimensions back to gene programs. Across four SERGIO simulated datasets and 13 epithelial-to-mesenchymal transition time-course datasets, its gene modules carry statistical filtering and GO enrichment annotation; on simulated data at k=10, background genes make up 7.0% of the 100 highest-attributed genes per latent, against a 73.3% chance level.

scCont breaks cell-state transitions in time-course single-cell RNA data into interpretable functional transition units: instead of explicit time labels or clustering priors, it selects positive pairs with a k-nearest-neighbor graph in expression space for contrastive learning, then uses Shapley attribution to map latent dimensions back to gene programs. Across four SERGIO simulated datasets and 13 epithelial-to-mesenchymal transition time-course datasets, its gene modules carry statistical filtering and GO enrichment annotation; on simulated data at k=10, background genes make up 7.0% of the 100 highest-attributed genes per latent, against a 73.3% chance level.

Nanoparticle neurological safety is hard to predict from material properties alone, because exposure conditions, disease context and host responses reshape outcomes. Across 893 experimental contexts from 599 neurological studies, pro-inflammatory, anti-inflammatory/resolution and apoptosis-associated responses were harmonized into a Conserved Response Axis and added layer by layer to material descriptors. With a Random Forest model, biosafety ROC-AUC rose from 0.66 with material properties alone to 0.88 with exposure and biological context and 0.90 with cellular responses; recovery rose from 0.64 to 0.77 and 0.78.

Nanoparticle neurological safety is hard to predict from material properties alone, because exposure conditions, disease context and host responses reshape outcomes. Across 893 experimental contexts from 599 neurological studies, pro-inflammatory, anti-inflammatory/resolution and apoptosis-associated responses were harmonized into a Conserved Response Axis and added layer by layer to material descriptors. With a Random Forest model, biosafety ROC-AUC rose from 0.66 with material properties alone to 0.88 with exposure and biological context and 0.90 with cellular responses; recovery rose from 0.64 to 0.77 and 0.78.

Machine-learned corrections to global buckling predictions for built-up cold-formed steel columns were previously judged on a random 80/20 split, where specimens from one test programme can sit in both training and test sets. Holding out entire experimental sources instead, across 148 tests from 15 sources and 13 section families, weakens transfer sharply: the best model, Lasso, reaches an $R^2$ of 0.787 and a mean absolute percentage error of 73.0%. Calibrated on those out-of-source predictions, LRFD resistance factors fall to 0.191–0.399 and ASD safety factors to 4.01–8.38.

Machine-learned corrections to global buckling predictions for built-up cold-formed steel columns were previously judged on a random 80/20 split, where specimens from one test programme can sit in both training and test sets. Holding out entire experimental sources instead, across 148 tests from 15 sources and 13 section families, weakens transfer sharply: the best model, Lasso, reaches an $R^2$ of 0.787 and a mean absolute percentage error of 73.0%. Calibrated on those out-of-source predictions, LRFD resistance factors fall to 0.191–0.399 and ASD safety factors to 4.01–8.38.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.