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| 1 | HIV模型的统计诊断显示文摘描述HIV的数学模型是一组非线性常微分方程,其中包括CD4+T细胞再感染率和死亡率,HIV病毒死亡率等多个重要未知参数,准确估计这些参数有助于正确了解患者病情发展,以采用个人化治疗方案.针对此模型已提出多个参数估计方法,包括基于数值解的非线性最小二乘,截面似然及两步估计.另一方面,由于客观因素影响,各观测数据对于参数估计的影响大小不同,找出对参数估计影响大的观测值,进一步分析其原因具有重要意义.基于HIV模型的两步估计,构造了用于影响分析的非参数Cook型统计量,并给出其大样本分布.通过模拟数据和临床数据分析发现:(1)所构造统计量可以检测中度以上偏移.(2)相对于内点,边界点对HIV模型的参数估计影响更大.基于以上结论,建议对HIV模型进行参数估计时,对边界点应加以格外关注.提出的方法也可以推广到其他线性常微分方程模型的统计诊断问题中. | 周杰 刘三阳 周芳 WU HuLin | 2012 | 科学通报2012，57，8： | 3 |
| 2 | Designing acceptance sampling schemes for life testing with mixed censoring 显示文摘 | CHEN Jianwei CHOU Winlin WU Hulin | 2004 | Naval Research Logistics2004，15，： | 1 |
| 3 | Designing acceptance sampling schemes for life testing with mixed censoring显示文摘 | CHEN Jianwei Chou W WU Hulin ZHOU Haibo | | 0，，： | 1 |
| 4 | Modeling the HIV epidemic:a state-space approach显示文摘 | Wai-Yuan Tan | 2000 | Mathematical and Computer modeling2000，32，： | 1 |
| 5 | Field-testing of synthetic herbivore-induced plant volatiles as attractants for beneficial insects 显示文摘 | YU Hulin ZHANG Yongjun WU Kongming | 2008 | Environ Entomol2008，37，6： | 1 |
| 6 | Modeling the HIV epidemic:a state-space approach显示文摘 | Wu Hulin Tan Wai-Yuan | 2000 | Mathl Computer model2000，32，12： | 1 |
| 7 | Modeling the HIV epidemic:a state-space apporach显示文摘 | Hulin Wu Wai-Yuan Tan | 2000 | Mathl Computer model2000，32，12： | 1 |
| 8 | Parameter Identifiability and Estimation of HIV/AIDS Dynamic Models 显示文摘 | Hulin Wu Haihong Zhu Hongyu Miao | 2008 | Bulletin of Mathematical Biology2008，70，2： | 1 |
| 9 | Modeling Long-Term HIV Dynamics and Antiretroviral Response:Effects of Drug Potency,Pharmacokinetics,Adherence,and Drug Resistance显示文摘 | Wu Hulin Huang Yangxin Edward A | 2005 | Journal of Acquired Immune Deficiency Syndromes2005，39，3： | 1 |
| 10 | Correlation-based iterative clustering methods for time course data:The identification of temporal gene response modules for influenza infection in humans显示文摘Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure.The availability of time course data has motivated researchers to develop methods,such as mixture and mixed-effects modelling approaches,that incorporate the temporal information contained in the shape of the trajectory of the data.However,there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters(some clusters are quite large and others are quite small).Here we propose two such methods,hierarchical clustering(IHC)and iterative pairwise-correlation clustering(IPC).We evaluate and compare the proposed methods to the Markov Cluster Algorithm(MCL)and the generalised mixed-effects model(GMM)using simulation studies and an application to a time course gene expression data set from a study containing human subjects who were challenged by a live influenza virus.We identify four types of temporal gene response modules to influenza infection in humans,i.e.,single-gene modules(SGM),small-size modules(SSM),mediumsize modules(MSM)and large-size modules(LSM).The LSM contain genes that perform various fundamental biological functions that are consistent across subjects.The SSM and SGM contain genes that perform either different or similar biological functions that have complex temporal responses to the virus and are unique to each subject.We show that the temporal response of the genes in the LSM have either simple patterns with a single peak or trough a consequence of the transient stimuli sustained or state-transitioning patterns pertaining to developmental cues and that these modules can differentiate the severity of disease outcomes.Additionally,the size of gene response modules follows a power-law distribution with a consistent exponent across all subjects,which reveals the presence of universality in the underlying biological principles that generated these modules. | Michelle Carey Shuang Wu Guojun Gan Hulin Wu | 2016 | Infectious Disease Modelling2016，1，1： | 0 |
| 11 | Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human显示文摘Background:Gene regulatory networks are complex dynamic systems and the reverseengineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields.It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms.We focus on the gene regulatory networks reconstructed using the ordinary differential equation(ODE)modelling approach and investigate the properties of these networks.Results:Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models.Symptomatic subjects tend to have larger numbers of driver nodes,higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects.We also show that the degree distribution,rather than the structure of networks,plays an important role in controlling the network in response to influenza infection.In addition,we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation.Conclusions:The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects.This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations,so these subjects are less likely to develop symptoms.Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters. | Xiaodian Sun Fang Hu Shuang Wu Xing Qiu Patrice Linel Hulin Wu | 2016 | Infectious Disease Modelling2016，1，1： | 0 |
| 12 | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model显示文摘While the Coronavirus Disease 2019(COVID-19)pandemic continues to threaten public health and safety,every state has strategically reopened the business in the United States.It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource allocations.In this study,a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in Texas.The earlier reported data before the reopening were used to develop the SEIR model;data after the reopening were used for evaluation.The simulation results show that if continuing the“stay-at-home order”without reopening the business,the COVID-19 pandemic could end in December 2020 in Texas.On the other hand,the pandemic could be controlled similarly as the case of noreopening only if the contact rate was low and additional high magnitude of control measures could be implemented.If the control measures are only slightly enhanced after reopening,it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths,but it might make the epidemic last longer.Based on the reported data up to July 2020 in Texas,the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after reopening.In this case,the pandemic might last until summer 2021 to February 2022 with a total of 4-10 million infected cases and 20,080e58,604 deaths. | Duo Yu Gen Zhu Xueying Wang Chenguang Zhang Babak Soltanalizadeh Xia Wang Sanyi Tang Hulin Wu | 2021 | Infectious Disease Modelling2021，6，1： | 0 |
| 13 | Investigation of temporal and spatial heterogeneities of the immune responses to Bordetella pertussis infection in the lung and spleen of mice via analysis and modeling of dynamic microarray gene expression data显示文摘Bordetella pertussis(B.pertussis)is the causative agent of pertussis,also referenced as whooping cough.Although pertussis has been appropriately controlled by routine immunization of infants,it has experienced a resurgence since the beginning of the 21st century.Given that elucidating the immune response to pertussis is a crucial factor to improve therapeutic and preventive treatments,we re-analyzed a time course microarray dataset of B.pertussis infection by applying a newly developed dynamic data analysis pipeline.Our results indicate that the immune response to B.pertussis is highly dynamic and heterologous across different organs during infection.Th1 and Th17 cells,which are two critical types of T helper cell populations in the immune response to B.pertussis,and follicular T helper cells(TFHs),which are also essential for generating antibodies,might be generated at different time points and distinct locations after infection.This phenomenon may indicate that different lymphoid organs may have their unique functions during infection.These findings provide a better understanding of the basic immunology of bacterial infection,which may provide valuable insights for the improvement of pertussis vaccine design in the future. | Nan Deng Juan C.Ramirez Michelle Carey Hongyu Miao Cesar A.Arias Andrew P.Rice Hulin Wu | 2019 | Infectious Disease Modelling2019，4，1： | 0 |
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