CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.
复制标题
CarcinoPred-EL：使用分子指纹和集成学习方法预测化学物质致癌性的新模型
DOI：
10.1038/s41598-017-02365-0
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发表时间：
2017-05-18
影响因子：
4.6
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Zhang L;Ai H;Chen W;Yin Z;Hu H;Zhu J;Zhao J;Zhao Q;Liu H
致癌性是指某些化学物质的高毒性终点，已成为药物开发过程中的重要问题。在这项研究中，开发了三种新的集成分类模型，即Ensemble SVM，Ensemble RF和Ensemble XGBoost，使用七种类型的分子指纹和三种机器学习方法，基于包含1003种具有大鼠致癌性的不同化合物的数据集，预测化学品的致癌性。在这三个模型中，Entrance XGBoost被认为是最好的，在五倍交叉验证中的平均准确度为70.1 ± 2.9%，灵敏度为67.0 ± 5.0%，特异性为73.1 ± 4.4%，在外部验证中的准确度为70.0%，灵敏度为65.2%，特异性为76.5%。与最近的一些方法相比，集成模型优于一些基于机器学习的方法，并产生相同的准确性和更高的特异性，但低于基于规则的专家系统的灵敏度。研究还发现，如果有更多的数据，集合模式可以进一步改进。作为一个应用，系综模型被用来发现潜在的致癌物在DrugBank数据库。结果表明，所提出的模型有助于预测化学品的致癌性。为这些模型建立了一个名为CarcinoPred-EL的网络服务器（http：//ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/）。
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).