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Ribosomal protein S6 kinase beta-1 (S6K1) is an attractive therapeutic target.

To solve the high cost of bridge load tests, considerable influence on traffic, and damage to bridge structure in the process of problem detection and maintenance of existing bridges, we use the method of improved Gaussian process response surface to explore the prediction of the rigidity and unit weight of existing bridges to produce high‐precision predictions of the existing bridge static test. The Gaussian process response surface method in Bayesian principle is adopted to optimize and correct the model. Because Gaussian process is a non‐parameter model with high sensitivity, reliable accuracy, and small calculation, it avoids repeatedly using finite element model. This makes the efficiency of finite element correction improve significantly. A continuous beam bridge is adopted as a study case. In sample selection, an improved uniform design method based on weighting is designed to improve the learning efficiency and prediction accuracy of the model while using ANSYS to analyze and predict the typical bridge. The analysis of the date of the bridge dynamic test is used to correct the finite element model, obtain the actual parameters of the bridge structure, and predict the measured data of the bridge static load test based on the modified finite element model.