Sensitivity analysis of 50:50 model. حسب Rachael Miller Neilan (10678614)

"…<p>A sensitivity analysis was conducted on the 50:50 model with constant 120 pA current to determine the sensitivity of pain output to select model parameters before injury (t = 10), during injury (t = 105), and after injury (t = 240). …"

Definitions of the features selected [50]. حسب Yu Zhang (12946)

Serum samples and 50% NAI values. Serum samples taken from CEIRS study. حسب Harrison Powell (9040128)

MBIC50 and MBEC50 values (µg/mL) of synthesized 1,2,3,5-tetrazine derivatives against bacteria pathogen strains. حسب Jean Paul Dzoyem (6424445)

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Velocity distribution at different openings (a) 25%; (b) 50%; (c) 75%; (d) 100%. حسب Weiqing Xu (1279959)

Expression of ApoB-50 decreased lipid accumulation and inhibited HCV production. حسب Bei Wang (116772)

Minimum quorum sensing inhibitory concentrations (MQSICs), minimum inhibitory concentrations (MICs), and concentrations inhibiting 50% of violacein production (IC50s) of compounds... حسب Jean Paul Dzoyem (6424445)

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Primary endpoint analysis, i.e., time to first occurrence of combined event of 50% decrease in eGFR or ESRD. حسب Rozita Mohd (7411322)

Split <i>ClvR</i>^<50cM configurations. حسب Georg Oberhofer (3401438)

Localized schematic of ball valves with different openings (a) 25%; (b) 50%; (c) 75%; (d) 100%. حسب Weiqing Xu (1279959)

Distribution of hydrogen volume fraction at different openings (a) 25%; (b) 50%; (c) 75%; (d) 100%. حسب Weiqing Xu (1279959)

Kylo-0603’s EC50 & tissue levels. حسب Xueqin lu (22250795)

Population dynamics modeling of split^50cM <i>ClvR</i> V1 introduced at a 50% release, with fitness cost per allele varying from 0–15%. حسب Georg Oberhofer (3401438)

Summary of subgroup analysis results. حسب Junyin Pan (20700411)

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Original study data. حسب Junyin Pan (20700411)

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S2 File - حسب Junyin Pan (20700411)

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Forest plot. حسب Junyin Pan (20700411)

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Funnel plot. حسب Junyin Pan (20700411)

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Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. حسب Dennis Ochola (11626912)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. حسب Dennis Ochola (11626912)