目的基于深度学习算法开发和验证可评估肝细胞癌(hepatocellular carcinoma,HCC)患者预后的预测模型,并评估其价值。方法选择2011年1月—2015年12月美国国立癌症研究所的监测、流行病学和最终结果(Surveillance,Epidemiology and Result...目的基于深度学习算法开发和验证可评估肝细胞癌(hepatocellular carcinoma,HCC)患者预后的预测模型,并评估其价值。方法选择2011年1月—2015年12月美国国立癌症研究所的监测、流行病学和最终结果(Surveillance,Epidemiology and Results,SEER)数据库中经病理确诊的HCC患者为训练队列用于构建模型,选择同期在本院经病理确诊的HCC患者为外部验证队列用于评估模型。主要预测结局为1、3、5年总生存率。分别使用深度学习算法DeepSurv、随机生存森林(RFS)、Cox比例风险回归开发模型,使用C-index评价模型的区分度,一致性曲线评估模型的校准度,log-rank检验评估危险分层能力。结果训练队列9730例患者和外部验证队列405例患者被纳入分析。在训练队列中,DeepSurv算法1、3、5年的C-index分别为0.85(95%CI:0.80~0.90)、0.82(95%CI:0.77~0.89)、0.80(95%CI:0.73~0.87),在外部验证队列中分别为0.83(95%CI:0.78~0.87)、0.79(95%CI:0.74~0.83)、0.72(95%CI:0.67~0.77)。无论在训练队列还是外部验证队列中,DeepSurv算法的C-index和校准度均优于RFS、Cox回归和TNM分期(均P<0.05);log-rank检验显示,DeepSurv算法具有良好的危险分层能力(P<0.001)。结论基于深度学习算法DeepSurv开发的预测模型可有效预测HCC患者死亡率,且优于常规的算法和预后评价指标。展开更多
Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has ...Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins(CDs)systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.展开更多
文摘目的基于深度学习算法开发和验证可评估肝细胞癌(hepatocellular carcinoma,HCC)患者预后的预测模型,并评估其价值。方法选择2011年1月—2015年12月美国国立癌症研究所的监测、流行病学和最终结果(Surveillance,Epidemiology and Results,SEER)数据库中经病理确诊的HCC患者为训练队列用于构建模型,选择同期在本院经病理确诊的HCC患者为外部验证队列用于评估模型。主要预测结局为1、3、5年总生存率。分别使用深度学习算法DeepSurv、随机生存森林(RFS)、Cox比例风险回归开发模型,使用C-index评价模型的区分度,一致性曲线评估模型的校准度,log-rank检验评估危险分层能力。结果训练队列9730例患者和外部验证队列405例患者被纳入分析。在训练队列中,DeepSurv算法1、3、5年的C-index分别为0.85(95%CI:0.80~0.90)、0.82(95%CI:0.77~0.89)、0.80(95%CI:0.73~0.87),在外部验证队列中分别为0.83(95%CI:0.78~0.87)、0.79(95%CI:0.74~0.83)、0.72(95%CI:0.67~0.77)。无论在训练队列还是外部验证队列中,DeepSurv算法的C-index和校准度均优于RFS、Cox回归和TNM分期(均P<0.05);log-rank检验显示,DeepSurv算法具有良好的危险分层能力(P<0.001)。结论基于深度学习算法DeepSurv开发的预测模型可有效预测HCC患者死亡率,且优于常规的算法和预后评价指标。
基金supported by the University of Macao Research Grants(MYRG2016-00038ICMS-QRCM and MYRG2016-00040-ICMS-QRCM,Macao,China).
文摘Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins(CDs)systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.
