The influence of geographical environment factors on reference value of serum uric acid in healthy people based on CatBoost model and SHAP analysis / 西安交通大学学报(医学版)

【Objective】 To explore the geographical environment factors that may affect serum uric acid (UA) of healthy people and explore the change trend of UA reference value at the national scale. 【Methods】 The UA reference values of 607905 healthy people from 565 loci in China were collected, and the correlation between 25 geographical environment factors and UA reference values was analyzed by correlation analysis. CatBoost model was constructed and SHAP value interpretation model was applied to predict the UA reference values of healthy people in counties and cities in China, and the geographical distribution map of UA reference values of healthy people in China was drawn by using ordinary Kriging. 【Results】 A total of 20 indicators, namely, latitude, altitude, annual average temperature, annual average relative humidity, annual precipitation, air temperature annual range, annual average wind speed, percentage of surface soil silt, surface soil bulk density, surface soil gravel content, surface soil organic matter content, surface soil PH, surface soil (clay) cation exchange capacity, surface soil (silt) cation exchange capacity, surface soil base saturation, total surface soil exchange capacity, T-CaCO3, T-CaSO4, surface soil alkalinity, and surface soil salt showed their correlation with UA reference value of healthy people nationwide. The spatial distribution of UA reference values of healthy people across the country differed, manifested as the changing trend of higher in high altitude regions, higher in coastal regions than in inland regions, lower in the mid-eastern region, and higher in Southwest China at similar altitudes. 【Conclusion】 This study lays a foundation for further studies on the mechanism of different influencing factors on UA reference value. CatBoost model was established to provide the basis for establishing reference standards using UA reference values as prognostic factors for hyperuricemia and related chronic diseases in different regions.

Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods / 中国药科大学学报

@#Inhibition of human cytochrome P450 (CYP) can lead to drug-drug interactions, resulting in serious adverse reactions.It is therefore crucial to accurately predict the inhibitory power of a given compound against a particular CYP isoform.This study compared 11 machine learning methods and 2 deep learning models based on different molecular representations.The experimental results showed that the CatBoost machine learning model based on RDKit_2d+Morgan outperformed other models in terms of accuracy and Mathews coefficient, and even outperformed previously published models.Moreover, the experimental results also showed that the CatBoost model not only had superior performance, but also consumed less computational resources.Finally, this study combined the top 3 performing models as co_model, which slightly outperformed the CatBoost model alone in terms of performance.