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Semisupervised D-Learning for Optimal Individualized Treatment Regimes

Stat 2025年第2期14卷

作者： Li, Xintong Zhang, Shuyi Zhou, Yong School of Statistics East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China Academy of Statistics and Interdisciplinary Sciences East China Normal University Shanghai China

In precision medicine, linear treatment decision classes have attracted widespread attention due to their simple structures and good interpretability. However, linear decision models may often be misspecified in practical applications. To address this issue, we develop an imputation-based semisupervised D-learning method that leverages information from unlabelled data to enhance the efficiency of estimating optimal individualized treatment regimes (ITRs), especially when the linear decision model is misspecified. Specifically, we estimate the imputation function using a projection-based dimension reduction approach, adjust for the bias in imputation estimates via a residual refitting step, and estimate the decision function based on the debiased imputation function. To mitigate potential bias due to overfitting, cross-validation is incorporated. Theoretical results show that the semisupervised D-learning method achieves (Formula presented.) -consistent parameter estimates with asymptotic normality. Numerical experiments on both simulated and real datasets demonstrate the superior performance of our proposed approach. © 2025 John Wiley & Sons Ltd.

关键词： D-learning least square estimation precision medicine semisupervised inference

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Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Single-cell omics: experimental workflow, data analyses and applications

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science China(Life sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Equilibrium Reinsurance Strategy and Mean Residual Life Function

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 758-777页

作者： Dan-ping LI Lv CHEN Lin-yi QIAN Wei WANG School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China Academy of Statistics and Interdisciplinary Sciences Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China School of Mathematics and Statistics Ningbo UniversityNingbo 315211China

In this paper,we analyze the relationship between the equilibrium reinsurance strategy and the tail of the distribution of the *** Mean Residual Life(MRL)has a close relationship with the tail of the distribution,we consider two classes of risk distributions,Decreasing Mean Residual Life(DMRL)and Increasing Mean Residual Life(IMRL)distributions,which can be used to classify light-tailed and heavy-tailed distributions,*** assume that the underlying risk process is modelled by the classical CramérLundberg model *** the mean-variance criterion,by solving the extended Hamilton-Jacobi-Bellman equation,we derive the equilibrium reinsurance strategy for the insurer and the reinsurer under DMRL and IMRL,***,we analyze how to choose the reinsurance premium to make the insurer and the reinsurer agree with the same reinsurance *** find that under the case of DMRL,if the distribution and the risk aversions satisfy certain conditions,the insurer and the reinsurer can adopt a reinsurance premium to agree on a reinsurance strategy,and under the case of IMRL,the insurer and the reinsurer can only agree with each other that the insurer do not purchase the reinsurance.

关键词： mean residual life excess-of-loss reinsurance insurer reinsurer stochastic control

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Functional Multiple-Outcome Model in application to Multivariate Growth Curves of Infant data

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Journal of Systems science & Complexity 2021年第4期34卷 1555-1577页

作者： YAN Xingyu ZHOU Yingchun PU Xiaolong ZHAO Peng School of Mathematics and Statistics Jiangsu Normal UniversityXuzhou 221116China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China

Motivated by a medical study that attempts to analyze the relationship between growth curves and other variables and to measure the association among multiple growth curves,the authors develop a functional multiple-outcome model to decompose the total variation of multiple functional outcomes into variation explained by independent variables with time-varying coefficient functions,by latent factors and by *** latent factors are the hidden common factors that influence the multiple outcomes and are found through the combined functional principal component analysis *** the coefficients of the latent factors one may further explore the association of the multiple *** method is applied to the multivariate growth data of infants in a real medical study in Shanghai and produces interpretable *** rates for the proposed estimates of the varying coefficient and covariance functions of the model are derived under mild conditions.

关键词： Convergence rate functional data functional principal components growth curve multiple outcomes

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The Conditional Mutual Information-Based Single-Sample Network Biomarker Approach Reveals the Critical Transition Moments of Disease Progression

SSRN

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SSRN 2025年

作者： Zhang, Lantian Hu, Fuyan Chi, Qingjia Chen, Bifeng The Department of Statistics School of Mathematics and Statistics Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Department of Mechanics and Engineering Structure Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China The Department of Biological Science and Technology School of Chemistry Chemical Engineering and Life Sciences Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China

During the progression of complex diseases, it is common to observe that the deterioration of the condition does not follow a smooth trajectory, and there is often a significant and critical transition from one state to another. Finding these critical transitions is of significant importance in the clinical treatment of cancer. In this study, we propose a novel computational approach, called the conditional mutual information-based single-sample network biomarker (CMISNB), which can reveal the critical transition moments of disease progression using only a single sample (https://***/ZLTSKY/CMISNB). By analyzing disease data from mouse acute lung injury, colon cancer, hepatocellular liver cancer, lung adenocarcinoma, and endometrial cancer, we validated the effectiveness of the CMISNB method during identifying critical transition moments in disease development. In particular, the CMISNB approach facilitates the discovery of new prognostic biomarkers and can provide personalized diagnoses for individuals. © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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Integrated Square Error of Hazard Rate Estimation for Survival data with Missing Censoring Indicators

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Journal of Systems science & Complexity 2021年第2期34卷 735-758页

作者： ZOU Yuye FAN Guoliang ZHANG Riquan School of Economics and Management Shanghai Maritime UniversityShanghai 201306China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China

The problem of hazard rate estimation under right-censored assumption has been investigated *** square error(ISE)of estimation is one of the most widely accepted measurements of the global performance for nonparametric kernel *** there are no results available for ISE of hazard rate estimation under right-censored model with censoring indicators missing at random(MAR)so *** paper constructs an imputation estimator of the hazard rate function and establish asymptotic normality of the ISE for the kernel hazard rate estimator with censoring indicators *** the same time,an asymptotic representation of the mean integrated square error(MISE)is also *** finite sample behavior of the estimator is investigated via one simple simulation.

关键词： Asymptotic normality integrated square error missing at random right-censored model

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A Two-Step Test for the Two-Sample Problem of Processes of Ornstein-Uhlenbeck Type

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Journal of Systems science & Complexity 2020年第6期33卷 2067-2079页

作者： WANG Yinfeng TANG Yanlin ZHANG Xinsheng School of Statistics and Mathematics Shanghai Lirin University of Acounting and FinanceShanghaiChina Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghaiChina Department of Statistics Fudan UniversityShanghaiChina

The authors propose a two-step test for the two-sample problem of processes of OrnsteinUhlenbeck type. In the first step, the authors test the equality of correlation structures, based on the least square estimators of the correlation parameters, and the test statistic follows the standard normal distribution. If the null hypothesis is not rejected in the first step, the authors consider a second step to test the equality of marginal distributions, based on the weighted deviation of the empirical characteristic functions;the test statistic has a complicated asymptotic distribution, so that sequential bootstrap method is applied to reach a temporary decision. Simulation studies and real data analysis suggest that the proposed approach performs well in finite samples.

关键词： Empirical characteristic functions hypothesis testing process of Ornstein-Uhlenbeck type sequential Bootstrap

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On the maximin distance properties of orthogonal designs via the rotation

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science China Mathematics 2023年第7期66卷 1593-1608页

作者： Yaping Wang Fasheng Sun Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationSchool of StatisticsEast China Normal UniversityShanghai 200062China Key Laboratory for Applied Statistics of Ministry of Education School of Mathematics and StatisticsNortheast Normal UniversityChangchun 130024China

Space-filling designs are widely used in computer *** are frequently evaluated by the orthogonality and distance-related *** orthogonal arrays is an appealing approach to constructing orthogonal space-filling *** important issue that has been rarely addressed in the literature is the design selection for the initial orthogonal *** paper studies the maximin L_(2)-distance properties of orthogonal designs generated by rotating two-level orthogonal arrays under three *** provide theoretical justifications for the rotation method from a maximin distance perspective and further propose to select initial orthogonal arrays by the minimum G_(2)-aberration *** infinite families of orthogonal or 3-orthogonal U-type designs,which also perform well under the maximin distance criterion,are obtained and *** are presented to show the effectiveness of the constructed designs for building statistical surrogate models.

关键词： computer experiment Latin hypercube design minimum G_(2)-aberration space-filling design U-type design

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Semiparametric Likelihood-based Inference for Censored data with Auxiliary Information from External Massive data Sources

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Acta Mathematicae Applicatae Sinica 2020年第3期36卷 642-656页

作者： Yue-xin FANG Yong ZHOU School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOEand Academy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062China

Published auxiliary information can be helpful in conducting statistical inference in a new *** this paper,we synthesize the auxiliary information with semiparametric likelihood-based inference for censoring data with the total sample size is *** express the auxiliary information as constraints on the regression coefficients and the covariate distribution,then use empirical likelihood method for general estimating equations to improve the efficiency of the interested parameters in the specified *** consistency and asymptotic normality of the resulting regression parameter estimators *** numerical simulation and application with different supposed conditions show that the proposed method yields a substantial gain in efficiency of the interested parameters.

关键词： Auxiliary information Massive data Censored data Empirical likelihood Estimation equations

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