In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model introduces an adaptive Gauss–Cauchy mutation operator to effectively increase the population diversity and search space and uses the improved algorithm to optimize the key parameters of the SVR model, so that the SVR model can mine the linear and nonlinear information in the data well. Secondly, the rolling prediction is integrated into the SVR prediction model, and the real-time update and self-regulation principles are used to continuously update the prediction, which greatly improves the prediction accuracy. Finally, the optimized STOA-SVR rolling forecast model is used to predict the final temperature. In this study, the global mean temperature data set from 1880 to 2022 is used for empirical analysis, and a comparative experiment is set up to verify the accuracy of the model. The results show that compared with the seasonal autoregressive integrated moving average (SARIMA), feedforward neural network (FNN) and unoptimized STOA-SVR-LSTM, the prediction performance of the proposed model is better, and the root mean square error is reduced by 6.33–29.62%. The mean relative error is reduced by 2.74–47.27%; the goodness of fit increases by 4.67–19.94%. Finally, the global mean temperature is predicted to increase by about 0.4976 °C in the next 20 years, with an increase rate of 3.43%. The model proposed in this paper not only has a good prediction accuracy, but also can provide an effective reference for the development and formulation of meteorological policies in the future.

Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs.