As weapon system effectiveness is affected by many factors,its evaluation is essentially a multi-criterion decision making problem for its complexity.The evaluation model of the effectiveness is established on the bas...As weapon system effectiveness is affected by many factors,its evaluation is essentially a multi-criterion decision making problem for its complexity.The evaluation model of the effectiveness is established on the basis of metrics architecture of the effectiveness.The Bayesian network,which is used to evaluate the effectiveness,is established based on the metrics architecture and the evaluation models.For getting the weights of the metrics by Bayesian network,subjective initial values of the weights are given,gradient ascent algorithm is adopted,and the reasonable values of the weights are achieved.And then the effectiveness of every weapon system project is gained.The weapon system,whose effectiveness is relative maximum,is the optimization system.The research result shows that this method can solve the problem of AHP method which evaluation results are not compatible to the practice results and overcome the shortcoming of neural network in multilayer and multi-criterion decision.The method offers a new approach for evaluating the effectiveness.展开更多
Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neura...Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neuraminidase(NA) inhibitors.The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses,and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance.Compound Yizhihao,which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China,however its mechanism of action against influenza is unclear.In this study,the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected,and multi-target QSAR(mt-QSAR) classification models were developed by Na?e Bayesian algorithm and verified by various datasets.Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao.This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.展开更多
文摘As weapon system effectiveness is affected by many factors,its evaluation is essentially a multi-criterion decision making problem for its complexity.The evaluation model of the effectiveness is established on the basis of metrics architecture of the effectiveness.The Bayesian network,which is used to evaluate the effectiveness,is established based on the metrics architecture and the evaluation models.For getting the weights of the metrics by Bayesian network,subjective initial values of the weights are given,gradient ascent algorithm is adopted,and the reasonable values of the weights are achieved.And then the effectiveness of every weapon system project is gained.The weapon system,whose effectiveness is relative maximum,is the optimization system.The research result shows that this method can solve the problem of AHP method which evaluation results are not compatible to the practice results and overcome the shortcoming of neural network in multilayer and multi-criterion decision.The method offers a new approach for evaluating the effectiveness.
基金supported by National Natural Science Foundation of China(81673480) Project of Urumqi science and Technology Bureau of the Xinjiang Uygur Autonomous Region(Y151310010)
文摘Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neuraminidase(NA) inhibitors.The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses,and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance.Compound Yizhihao,which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China,however its mechanism of action against influenza is unclear.In this study,the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected,and multi-target QSAR(mt-QSAR) classification models were developed by Na?e Bayesian algorithm and verified by various datasets.Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao.This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.
