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您的检索式：作者名="Fangnan Lin"
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| 1 | Resource Allocation Based on DEA and Non-Cooperative Game显示文摘Resource allocation is one of the most important applications of data envelopment analysis(DEA).Usually,the resource to be allocated is directly related to the interests of decision-making units(DMUs),thus the dynamic non-cooperative game is one of the representative behaviours in the allocation process.However,it is rarely considered in the previous DEA-based allocation studies,which may reduce the acceptability of the allocation plan.Therefore,this paper proposes a DEA-based resource allocation method considering the dynamic non-cooperative game behaviours of DMUs.The authors first deduce the efficient allocation set under the framework of variable return to scale(VRS)and build the allocation model subjecting to the allocation set.Then an iteration algorithm based on the concept of the non-cooperative game is provided for generating the optimal allocation plan.Several interesting characteristics of the algorithm are proved,including i)the algorithm is convergent,ii)the optimal allocation plan is a unique Nash equilibrium point,and iii)the optimal allocation plan is unique no matter which positive value the initial allocation takes.Some advantages of the allocation plan have been found.For example,the allocation plan is more balanced,has more incentives and less outliers,compared with other DEA-based allocation plans.Finally,the proposed method is applied to allocate the green credit among the 30 Chinese iron and steel enterprises,and the results highlight the applicability of the allocation method and solution approach.Therefore,the approach can provide decision makers with a useful resource allocation tool from the perspective of dynamic non-cooperative game. | WANG Menghan LI Lin DAI Qianzhi SHI Fangnan | 2021 | Journal of Systems Science & Complexity2021，34，6： | 1 |
| 2 | A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma显示文摘Background:The immune response in the tumor microenvironment(TME)plays a crucial role in cancer progression and recurrence.We aimed to develop an immune-related gene(IRG)signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma(PDAC).Methods:The Cancer Genome Atlas(TCGA)PDAC was used to construct a prognostic model as a training cohort.The International Cancer Genome Consortium(ICGC)and the Gene Expression Omnibus(GEO)databases were set as validation datasets.Prognostic genes were screened by using univariate Cox regression.Then,a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator(LASSO)Cox regression.Cell type identification by estimating the relative subsets of RNA transcripts(CIBERSORT)and estimation of stromal and immune cells in malignant tumors using expression data(ESTIMATE)algorithms were used to characterize tumor immune infiltrating patterns.The tumor immune dysfunction and exclusion(TIDE)algorithm was used to predict immunotherapy responsiveness.Results:A prognostic signature based on five IRGs(MET,ERAP2,IL20RB,EREG,and SHC2)was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts.Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value.The area under the curve(AUC)values of the receiver operating characteristic(ROC)curve at 1,3,and 5 years of survival were 0.724,0.702,and 0.776,respectively.We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification(TNM)stage in predicting survival.Moreover,we found higher abundance of CD8+T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC,and predicted a higher proportion of immunotherapeutic responders in the low-risk group.Conclusions:We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases.Additionally,this five-genes signature could predict immune infiltration characteristics.Moreover,the signature helped stratify PDAC patients who might be more responsive to immunotherapy. | Ang Li Bicheng Ye Fangnan Lin Yilin Wang Xiaye Miao Yanfang Jiang | 2022 | Precision Clinical Medicine2022，5，2： | 1 |
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