基于大脑情感网络模型与PSO-AGA算法对抗乳腺癌候选药物ERα的研究
Research Based on Brain Affective Network Model and PSO-AGA Algorithm against Breast Cancer Drug Candidate ERα

乳腺癌是目前世界上最常见，致死率较高的癌症之一。有研究发现，能够拮抗ERα活性的化合物可能是治疗乳腺癌的候选药物，具有重要的研究价值。本文从理想到实际，首先使用模型对数据进行清洗和筛选，之后基于大脑情感网络模型和PSO-AGA算法对ERα生物活性进行预测，再使用多元线性回归模型、支持向量机、随机森林模型和梯度提升树回归模型进行预测模型构建并求解，并对五种模型的预测效果进行分析。
Breast cancer is currently one of the most common cancers in the world with a high fatality rate. Studies have found that compounds that can antagonize the activity of ERα may be drug candidates for the treatment of breast cancer and have important research value. From ideal to practical, this paper first uses the model to clean and screen the data, and then predicts the biological activity of ERα based on the brain affective network model and PSO-AGA algorithm, and then uses multiple linear regression model, support vector machine, random forest model and gradient boosted tree regression model to construct and solve the prediction model, and analyzes the prediction effect of the five models.