| Year |
Citation |
Score |
| 2021 |
Fan X, Zhang Q, Ma S, Fang K. Conditional score matching for high-dimensional partial graphical models Computational Statistics & Data Analysis. 153: 107066. DOI: 10.1016/J.Csda.2020.107066 |
0.373 |
|
| 2020 |
Fang K, Zhang X, Ma S, Zhang Q. Smooth and Locally Sparse Estimation for Multiple-Output Functional Linear Regression. Journal of Statistical Computation and Simulation. 90: 341-354. PMID 33012883 DOI: 10.1080/00949655.2019.1680676 |
0.313 |
|
| 2020 |
Zhang S, Fan Y, Zhong T, Ma S. Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling. Scientific Reports. 10: 15030. PMID 32929170 DOI: 10.1038/S41598-020-72201-5 |
0.385 |
|
| 2020 |
Liu M, Zhang Q, Fang K, Ma S. Structured Analysis of the High-dimensional FMR Model. Computational Statistics & Data Analysis. 144. PMID 32863493 DOI: 10.1016/J.Csda.2019.106883 |
0.398 |
|
| 2020 |
He B, Zhong T, Huang J, Liu Y, Zhang Q, Ma S. Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging. Biometrics. PMID 32822084 DOI: 10.1111/Biom.13357 |
0.374 |
|
| 2020 |
Wu M, Yi H, Ma S. Vertical integration methods for gene expression data analysis. Briefings in Bioinformatics. PMID 32793970 DOI: 10.1093/Bib/Bbaa169 |
0.337 |
|
| 2020 |
Fan X, Fang K, Ma S, Zhang Q. Integrating approximate single factor graphical models. Statistics in Medicine. 39: 146-155. PMID 31749227 DOI: 10.1002/Sim.8408 |
0.408 |
|
| 2019 |
Zhang X, Zhang Q, Wang X, Ma S, Fang K. Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers. Statistics in Medicine. PMID 31880351 DOI: 10.1002/Sim.8454 |
0.349 |
|
| 2019 |
Ren J, Zhou F, Li X, Chen Q, Zhang H, Ma S, Jiang Y, Wu C. Semiparametric Bayesian variable selection for gene-environment interactions. Statistics in Medicine. PMID 31863500 DOI: 10.1002/Sim.8434 |
0.368 |
|
| 2019 |
Zhang S, Xue Y, Zhang Q, Ma C, Wu M, Ma S. Identification of gene-environment interactions with marginal penalization. Genetic Epidemiology. PMID 31724772 DOI: 10.1002/Gepi.22270 |
0.375 |
|
| 2019 |
Ma C, Xue Y, Ma S. Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing. Cancers. 11. PMID 31694302 DOI: 10.3390/Cancers11111732 |
0.309 |
|
| 2019 |
Wu M, Zhang Q, Ma S. Structured gene-environment interaction analysis. Biometrics. PMID 31424088 DOI: 10.1111/Biom.13139 |
0.329 |
|
| 2019 |
Wang S, Wu M, Ma S. Integrative Analysis of Cancer Omics Data for Prognosis Modeling. Genes. 10: 604. PMID 31405076 DOI: 10.3390/Genes10080604 |
0.35 |
|
| 2019 |
Wu M, Ma S. Robust semiparametric gene-environment interaction analysis using sparse boosting. Statistics in Medicine. PMID 31359454 DOI: 10.1002/Sim.8322 |
0.382 |
|
| 2019 |
Sun Y, Sun Z, Jiang Y, Li Y, Ma S. An integrative sparse boosting analysis of cancer genomic commonality and difference. Statistical Methods in Medical Research. 962280219859026. PMID 31282286 DOI: 10.1177/0962280219859026 |
0.398 |
|
| 2019 |
Chai H, Zhang Q, Huang J, Ma S. INFERENCE FOR LOW-DIMENSIONAL COVARIATES IN A HIGH-DIMENSIONAL ACCELERATED FAILURE TIME MODEL. Statistica Sinica. 29: 877-894. PMID 31073263 DOI: 10.5705/Ss.202016.0449 |
0.36 |
|
| 2019 |
Xu Y, Zhong T, Wu M, Ma S. Histopathological Imaging⁻Environment Interactions in Cancer Modeling. Cancers. 11. PMID 31022926 DOI: 10.3390/Cancers11040579 |
0.365 |
|
| 2019 |
Li Y, Li R, Lin C, Qin Y, Ma S. Penalized integrative semiparametric interaction analysis for multiple genetic datasets. Statistics in Medicine. PMID 30993736 DOI: 10.1002/Sim.8172 |
0.403 |
|
| 2019 |
Zhong T, Wu M, Ma S. Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer. Cancers. 11. PMID 30871256 DOI: 10.3390/Cancers11030361 |
0.338 |
|
| 2019 |
Fan X, Fang K, Ma S, Wang S, Zhang Q. Assisted graphical model for gene expression data analysis. Statistics in Medicine. PMID 30854706 DOI: 10.1002/Sim.8112 |
0.368 |
|
| 2019 |
Ren J, Du Y, Li S, Ma S, Jiang Y, Wu C. Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis. Genetic Epidemiology. PMID 30746793 DOI: 10.1002/Gepi.22194 |
0.397 |
|
| 2019 |
Wang X, Xu Y, Ma S. Identifying gene-environment interactions incorporating prior information. Statistics in Medicine. PMID 30637789 DOI: 10.1002/Sim.8064 |
0.332 |
|
| 2019 |
Shi X, Ma S, Huang Y. Promoting sign consistency in the cure model estimation and selection. Statistical Methods in Medical Research. 962280218820356. PMID 30600776 DOI: 10.1177/0962280218820356 |
0.361 |
|
| 2019 |
Liu J, Wang K, Ma S, Huang J. Accounting for linkage disequilibrium in genome-wide association studies: A penalized regression method. Statistics and Its Interface. 6: 99-115. PMID 25258655 DOI: 10.4310/Sii.2013.V6.N1.A10 |
0.344 |
|
| 2019 |
Liu J, Huang J, Ma S. Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization. Scandinavian Journal of Statistics, Theory and Applications. 41: 87-103. PMID 24578589 DOI: 10.1111/J.1467-9469.2012.00816.X |
0.431 |
|
| 2019 |
Huang J, Breheny P, Ma S. A Selective Review of Group Selection in High-Dimensional Models. Statistical Science : a Review Journal of the Institute of Mathematical Statistics. 27. PMID 24174707 DOI: 10.1214/12-Sts392 |
0.323 |
|
| 2019 |
Du P, Ma S, Liang H. PENALIZED VARIABLE SELECTION PROCEDURE FOR COX MODELS WITH SEMIPARAMETRIC RELATIVE RISK. Annals of Statistics. 38: 2092-2117. PMID 20802853 DOI: 10.1214/09-Aos780 |
0.35 |
|
| 2018 |
Wu C, Zhang Q, Jiang Y, Ma S. Robust network-based analysis of the associations between (epi)genetic measurements. Journal of Multivariate Analysis. 168: 119-130. PMID 30983643 DOI: 10.1016/J.Jmva.2018.06.009 |
0.399 |
|
| 2018 |
Xu Y, Wu M, Ma S, Ahmed SE. Robust gene-environment interaction analysis using penalized trimmed regression. Journal of Statistical Computation and Simulation. 88: 3502-3528. PMID 30718937 DOI: 10.1080/00949655.2018.1523411 |
0.306 |
|
| 2018 |
Sun Y, Jiang Y, Li Y, Ma S. Identification of cancer omics commonality and difference via community fusion. Statistics in Medicine. PMID 30421444 DOI: 10.1002/Sim.8027 |
0.358 |
|
| 2018 |
Hidalgo SJT, Zhu T, Wu M, Ma S. Overlapping clustering of gene expression data using penalized weighted normalized cut. Genetic Epidemiology. 42: 796-811. PMID 30302823 DOI: 10.1002/Gepi.22164 |
0.329 |
|
| 2018 |
Li Y, Bie R, Teran Hidalgo SJ, Qin Y, Wu M, Ma S. Assisted gene expression-based clustering with AWNCut. Statistics in Medicine. PMID 30094873 DOI: 10.1002/Sim.7928 |
0.327 |
|
| 2018 |
Xu Y, Wu M, Zhang Q, Ma S. Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach. Genomics. PMID 30009922 DOI: 10.1016/J.Ygeno.2018.07.006 |
0.371 |
|
| 2018 |
Wu M, Ma S. Robust genetic interaction analysis. Briefings in Bioinformatics. PMID 29897421 DOI: 10.1093/Bib/Bby033 |
0.33 |
|
| 2018 |
Teran Hidalgo SJ, Ma S. Clustering multilayer omics data using MuNCut. Bmc Genomics. 19: 198. PMID 29703159 DOI: 10.1186/S12864-018-4580-6 |
0.334 |
|
| 2018 |
Zhong W, Liu X, Ma S. Variable selection and direction estimation for single-index models via DC-TGDR method Statistics and Its Interface. 11: 169-181. DOI: 10.4310/Sii.2018.V11.N1.A14 |
0.3 |
|
| 2018 |
Fang K, Fan X, Zhang Q, Ma S. Integrative sparse principal component analysis Journal of Multivariate Analysis. 166: 1-16. DOI: 10.1016/J.Jmva.2018.02.002 |
0.383 |
|
| 2017 |
Jiang Y, Huang Y, Du Y, Zhao Y, Ren J, Ma S, Wu C. Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach. Cancer Informatics. 16: 1176935116684825. PMID 33354107 DOI: 10.1177/1176935116684825 |
0.33 |
|
| 2017 |
Chai H, Zhang Q, Jiang Y, Wang G, Zhang S, Ahmed SE, Ma S. Identifying gene-environment interactions for prognosis using a robust approach. Econometrics and Statistics. 4: 105-120. PMID 31157309 DOI: 10.1016/J.Ecosta.2016.10.004 |
0.428 |
|
| 2017 |
Huang Y, Zhang Q, Zhang S, Huang J, Ma S. Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization. Journal of the American Statistical Association. 112: 342-350. PMID 30100648 DOI: 10.1080/01621459.2016.1139497 |
0.421 |
|
| 2017 |
Yue M, Li J, Ma S. Sparse boosting for high-dimensional survival data with varying coefficients. Statistics in Medicine. PMID 29152776 DOI: 10.1002/Sim.7544 |
0.395 |
|
| 2017 |
Liu M, Fan X, Fang K, Zhang Q, Ma S. Integrative sparse principal component analysis of gene expression data. Genetic Epidemiology. PMID 29114920 DOI: 10.1002/Gepi.22089 |
0.4 |
|
| 2017 |
Wu M, Huang J, Ma S. Identifying gene-gene interactions using penalized tensor regression. Statistics in Medicine. PMID 29034516 DOI: 10.1002/Sim.7523 |
0.423 |
|
| 2017 |
Wu C, Jiang Y, Ren J, Cui Y, Ma S. Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine. PMID 29034484 DOI: 10.1002/Sim.7518 |
0.397 |
|
| 2017 |
Chai H, Shi X, Zhang Q, Zhao Q, Huang Y, Ma S. Analysis of cancer gene expression data with an assisted robust marker identification approach. Genetic Epidemiology. PMID 28913902 DOI: 10.1002/Gepi.22066 |
0.402 |
|
| 2017 |
Teran Hidalgo SJ, Wu M, Ma S. Assisted clustering of gene expression data using ANCut. Bmc Genomics. 18: 623. PMID 28814280 DOI: 10.1186/S12864-017-3990-1 |
0.37 |
|
| 2017 |
Wu M, Zang Y, Zhang S, Huang J, Ma S. Accommodating missingness in environmental measurements in gene-environment interaction analysis. Genetic Epidemiology. PMID 28657194 DOI: 10.1002/Gepi.22055 |
0.363 |
|
| 2017 |
Zhang Q, Duan X, Ma S. Focused Information Criterion and Model Averaging with Generalized Rank Regression. Statistics & Probability Letters. 122: 11-19. PMID 28566799 DOI: 10.1016/J.Spl.2016.10.020 |
0.312 |
|
| 2017 |
Fan X, Liu M, Fang K, Huang Y, Ma S. Promoting structural effects of covariates in the cure rate model with penalization. Statistical Methods in Medical Research. 962280217708684. PMID 28480830 DOI: 10.1177/0962280217708684 |
0.385 |
|
| 2017 |
Zang Y, Zhao Q, Zhang Q, Li Y, Zhang S, Ma S. Inferring gene regulatory relationships with a high-dimensional robust approach. Genetic Epidemiology. PMID 28464328 DOI: 10.1002/Gepi.22047 |
0.392 |
|
| 2017 |
Zhang Q, Zhang S, Liu J, Huang J, Ma S. Penalized integrative analysis under the accelerated failure time model Statistica Sinica. DOI: 10.5705/Ss.2014.194 |
0.423 |
|
| 2016 |
Fang K, Ma S. Analyzing large datasets with bootstrap penalization. Biometrical Journal. Biometrische Zeitschrift. PMID 27870109 DOI: 10.1002/Bimj.201600052 |
0.351 |
|
| 2016 |
Zhu R, Zhao YQ, Chen G, Ma S, Zhao H. Greedy outcome weighted tree learning of optimal personalized treatment rules. Biometrics. PMID 27704531 DOI: 10.1111/Biom.12593 |
0.336 |
|
| 2016 |
Huang Y, Liu J, Yi H, Shia BC, Ma S. Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data. Statistics in Medicine. PMID 27667129 DOI: 10.1002/Sim.7138 |
0.413 |
|
| 2016 |
Liu J, Yang C, Shi X, Li C, Huang J, Zhao H, Ma S. Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model. Genetic Epidemiology. PMID 27247027 DOI: 10.1002/Gepi.21975 |
0.379 |
|
| 2016 |
Liu J, Wan X, Ma S, Yang C. EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes. Bioinformatics (Oxford, England). PMID 27153687 DOI: 10.1093/Bioinformatics/Btw081 |
0.338 |
|
| 2016 |
Jiang Y, Shi X, Zhao Q, Krauthammer M, Rothberg BE, Ma S. Integrated analysis of multidimensional omics data on cutaneous melanoma prognosis. Genomics. PMID 27141884 DOI: 10.1016/J.Ygeno.2016.04.005 |
0.371 |
|
| 2016 |
Zhu R, Zhao Q, Zhao H, Ma S. Integrating multidimensional omics data for cancer outcome. Biostatistics (Oxford, England). PMID 26980320 DOI: 10.1093/Biostatistics/Kxw010 |
0.389 |
|
| 2016 |
Li J, Huang Z, Ma S, Lee MLT. Collective versus Individual Effects in Survival Analysis of Multiple Failures Scandinavian Journal of Statistics. 43: 543-557. DOI: 10.1111/Sjos.12190 |
0.383 |
|
| 2016 |
Qin L, Ma S, Lin JC, Shia BC. Lasso Regression Based on Empirical Mode Decomposition Communications in Statistics: Simulation and Computation. 45: 1281-1294. DOI: 10.1080/03610918.2013.826361 |
0.334 |
|
| 2016 |
Li Y, Qin Y, Wang L, Chen J, Ma S. Grouped Variable Selection Using Area under the ROC with Imbalanced Data Communications in Statistics: Simulation and Computation. 45: 1268-1280. DOI: 10.1080/03610918.2013.818691 |
0.33 |
|
| 2016 |
Fang K, Wang X, Shia BC, Ma S. Identification of proportionality structure with two-part models using penalization Computational Statistics and Data Analysis. 99: 12-24. DOI: 10.1016/J.Csda.2016.01.002 |
0.357 |
|
| 2016 |
Fu Z, Ma S, Lin H, Parikh CR, Zhou B. Penalized Variable Selection for Multi-center Competing Risks Data Statistics in Biosciences. 9: 379-405. DOI: 10.1007/S12561-016-9181-9 |
0.336 |
|
| 2015 |
Zeng X, Ma S, Qin Y, Li Y. Variable selection in strong hierarchical semiparametric models for longitudinal data Statistics and Its Interface. 8: 355-365. PMID 27076867 DOI: 10.4310/Sii.2015.V8.N3.A9 |
0.38 |
|
| 2015 |
Shi X, Zhao Q, Huang J, Xie Y, Ma S. Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics (Oxford, England). PMID 26342102 DOI: 10.1093/Bioinformatics/Btv518 |
0.309 |
|
| 2015 |
Wu C, Shi X, Cui Y, Ma S. A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine. PMID 26239060 DOI: 10.1002/Sim.6609 |
0.377 |
|
| 2015 |
Wang Z, Ma S, Wang CY. Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany. Biometrical Journal. Biometrische Zeitschrift. PMID 26059498 DOI: 10.1002/Bimj.201400143 |
0.362 |
|
| 2015 |
Zhao Q, Shi X, Huang J, Liu J, Li Y, Ma S. Integrative Analysis of "-Omics" Data Using Penalty Functions. Wiley Interdisciplinary Reviews. Computational Statistics. 7: 99-108. PMID 25691921 DOI: 10.1002/Wics.1322 |
0.349 |
|
| 2015 |
Wu C, Ma S. A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics. 16: 873-83. PMID 25479793 DOI: 10.1093/Bib/Bbu046 |
0.374 |
|
| 2015 |
Zhao Q, Shi X, Xie Y, Huang J, Shia B, Ma S. Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA. Briefings in Bioinformatics. 16: 291-303. PMID 24632304 DOI: 10.1093/Bib/Bbu003 |
0.368 |
|
| 2015 |
Yu T, Li J, Ma S. Accounting for clinical covariates and interactions in ranking genomic markers using ROC Communications in Statistics - Simulation and Computation. 46: 3735-3755. DOI: 10.1080/03610918.2015.1105972 |
0.396 |
|
| 2015 |
Li Y, Yu C, Qin Y, Wang L, Chen J, Yi D, Shia BC, Ma S. Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion Journal of Statistical Computation and Simulation. 85: 2582-2595. DOI: 10.1080/00949655.2014.899362 |
0.36 |
|
| 2014 |
Song R, Lu W, Ma S, Jeng XJ. Censored Rank Independence Screening for High-dimensional Survival Data. Biometrika. 101: 799-814. PMID 25663709 DOI: 10.1093/Biomet/Asu047 |
0.331 |
|
| 2014 |
Shi X, Yi H, Ma S. Measures for the degree of overlap of gene signatures and applications to TCGA. Briefings in Bioinformatics. PMID 25552438 DOI: 10.1093/Bib/Bbu049 |
0.373 |
|
| 2014 |
Liu J, Huang J, Ma S. Penalized multivariate linear mixed model for longitudinal genome-wide association studies. Bmc Proceedings. 8: S73. PMID 25519343 DOI: 10.1186/1753-6561-8-S1-S73 |
0.393 |
|
| 2014 |
Wang Y, Ma S. Racial differences in mantle cell lymphoma in the United States Bmc Cancer. 14: 764-764. PMID 25315847 DOI: 10.1186/1471-2407-14-764 |
0.315 |
|
| 2014 |
Wang Z, Ma S, Wang CY, Zappitelli M, Devarajan P, Parikh C. EM for regularized zero-inflated regression models with applications to postoperative morbidity after cardiac surgery in children. Statistics in Medicine. 33: 5192-208. PMID 25256715 DOI: 10.1002/Sim.6314 |
0.328 |
|
| 2014 |
Wu C, Cui Y, Ma S. Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model. Statistics in Medicine. 33: 4988-98. PMID 25146388 DOI: 10.1002/Sim.6287 |
0.363 |
|
| 2014 |
Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S. Integrative analysis of prognosis data on multiple cancer subtypes. Biometrics. 70: 480-8. PMID 24766212 DOI: 10.1111/Biom.12177 |
0.428 |
|
| 2014 |
Zhu R, Zhao H, Ma S. Identifying gene-environment and gene-gene interactions using a progressive penalization approach. Genetic Epidemiology. 38: 353-68. PMID 24723356 DOI: 10.1002/Gepi.21807 |
0.372 |
|
| 2014 |
Shi X, Liu J, Huang J, Zhou Y, Xie Y, Ma S. A penalized robust method for identifying gene-environment interactions. Genetic Epidemiology. 38: 220-30. PMID 24616063 DOI: 10.1002/Gepi.21795 |
0.414 |
|
| 2014 |
Shi X, Liu J, Huang J, Zhou Y, Shia B, Ma S. Integrative analysis of high-throughput cancer studies with contrasted penalization. Genetic Epidemiology. 38: 144-51. PMID 24395534 DOI: 10.1002/Gepi.21781 |
0.444 |
|
| 2014 |
Xiao G, Ma S, Minna J, Xie Y. Adaptive prediction model in prospective molecular signature-based clinical studies. Clinical Cancer Research : An Official Journal of the American Association For Cancer Research. 20: 531-9. PMID 24323903 DOI: 10.1158/1078-0432.Ccr-13-2127 |
0.328 |
|
| 2014 |
Shao F, Li J, Ma S, Lee ML. Semiparametric varying-coefficient model for interval censored data with a cured proportion. Statistics in Medicine. 33: 1700-12. PMID 24302535 DOI: 10.1002/Sim.6054 |
0.384 |
|
| 2014 |
Shi X, Shen S, Liu J, Huang J, Zhou Y, Ma S. Similarity of markers identified from cancer gene expression studies: observations from GEO. Briefings in Bioinformatics. 15: 671-84. PMID 23788798 DOI: 10.1093/Bib/Bbt044 |
0.392 |
|
| 2013 |
Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S. Identification of gene-environment interactions in cancer studies using penalization. Genomics. 102: 189-94. PMID 23994599 DOI: 10.1016/J.Ygeno.2013.08.006 |
0.403 |
|
| 2013 |
Liu J, Huang J, Xie Y, Ma S. Sparse group penalized integrative analysis of multiple cancer prognosis datasets. Genetics Research. 95: 68-77. PMID 23938111 DOI: 10.1017/S0016672313000086 |
0.418 |
|
| 2013 |
Liu J, Huang J, Ma S. Integrative analysis of multiple cancer genomic datasets under the heterogeneity model. Statistics in Medicine. 32: 3509-21. PMID 23519988 DOI: 10.1002/Sim.5780 |
0.41 |
|
| 2013 |
Ma S, Huang Y, Huang J, Fang K. Gene network-based cancer prognosis analysis with sparse boosting. Genetics Research. 94: 205-21. PMID 22950901 DOI: 10.1017/S0016672312000419 |
0.379 |
|
| 2012 |
Ma S, Du P. Variable Selection In Partly Linear Regression Model With Diverging Dimensions For Right Censored Data. Statistica Sinica. 22: 1003-1020. PMID 23956611 DOI: 10.5705/Ss.2010.267 |
0.391 |
|
| 2012 |
Liu H, Ma S, Kronmal R, Chan KS. SEMIPARAMETRIC ZERO-INFLATED MODELING IN MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA). The Annals of Applied Statistics. 6: 1236-1255. PMID 23805172 DOI: 10.1214/11-Aoas534 |
0.368 |
|
| 2012 |
Huang J, Wei F, Ma S. Semiparametric Regression Pursuit. Statistica Sinica. 22: 1403-1426. PMID 23559831 DOI: 10.5705/Ss.2010.298 |
0.367 |
|
| 2012 |
Liu J, Huang J, Ma S. Analysis of genome-wide association studies with multiple outcomes using penalization. Plos One. 7: e51198. PMID 23272092 DOI: 10.1371/Journal.Pone.0051198 |
0.354 |
|
| 2012 |
Liu J, Huang J, Ma S. Incorporating network structure in integrative analysis of cancer prognosis data. Genetic Epidemiology. 37: 173-83. PMID 23161517 DOI: 10.1002/Gepi.21697 |
0.418 |
|
| 2012 |
Liu J, Huang J, Ma S, Wang K. Incorporating group correlations in genome-wide association studies using smoothed group Lasso. Biostatistics (Oxford, England). 14: 205-19. PMID 22988281 DOI: 10.1093/Biostatistics/Kxs034 |
0.317 |
|
| 2012 |
Song X, Zhou XH, Ma S. Nonparametric receiver operating characteristic-based evaluation for survival outcomes. Statistics in Medicine. 31: 2660-75. PMID 22987578 DOI: 10.1002/Sim.5386 |
0.318 |
|
| 2012 |
Ma S, Zhang Y, Huang J, Huang Y, Lan Q, Rothman N, Zheng T. Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent. Genetic Epidemiology. 36: 829-38. PMID 22851516 DOI: 10.1002/Gepi.21669 |
0.413 |
|
| 2012 |
Song R, Huang J, Ma S. Integrative prescreening in analysis of multiple cancer genomic studies. Bmc Bioinformatics. 13: 168. PMID 22799431 DOI: 10.1186/1471-2105-13-168 |
0.346 |
|
| 2012 |
Ma S, Dai Y, Huang J, Xie Y. Identification of Breast Cancer Prognosis Markers via Integrative Analysis. Computational Statistics & Data Analysis. 56: 2718-2728. PMID 22773869 DOI: 10.1016/J.Csda.2012.02.017 |
0.39 |
|
| 2012 |
Dai Y, Ma S. Variable selection for semiparametric regression models with iterated penalization. Journal of Nonparametric Statistics. 24: 283-298. PMID 22685376 DOI: 10.1080/10485252.2012.661054 |
0.314 |
|
| 2012 |
Yu T, Li J, Ma S. Adjusting confounders in ranking biomarkers: a model-based ROC approach. Briefings in Bioinformatics. 13: 513-23. PMID 22396461 DOI: 10.1093/Bib/Bbs008 |
0.418 |
|
| 2012 |
Ma S, Huang J, Xie Y, Yi N. Identification of breast cancer prognosis markers using Integrative sparse Boosting Methods of Information in Medicine. 51: 152-161. PMID 22344268 DOI: 10.3414/Me11-02-0019 |
0.413 |
|
| 2012 |
Aschebrook-Kilfoy B, Zheng T, Foss F, Ma S, Han X, Lan Q, Holford T, Chen Y, Leaderer B, Rothman N, Zhang Y. Polymorphisms in immune function genes and non-Hodgkin lymphoma survival. Journal of Cancer Survivorship : Research and Practice. 6: 102-14. PMID 22113576 DOI: 10.1007/S11764-010-0164-4 |
0.324 |
|
| 2011 |
Liu A, Kronmal R, Zhou X, Ma S. Determination of proportionality in two-part models and analysis of Multi-Ethnic Study of Atherosclerosis (MESA). Statistics and Its Interface. 4: 475-487. PMID 23772262 DOI: 10.4310/Sii.2011.V4.N4.A6 |
0.337 |
|
| 2011 |
Liu J, Wang K, Ma S, Huang J. Regularized regression method for genome-wide association studies. Bmc Proceedings. S67. PMID 22373491 DOI: 10.1186/1753-6561-5-S9-S67 |
0.335 |
|
| 2011 |
Ma S, Huang J, Wei F, Xie Y, Fang K. Integrative analysis of multiple cancer prognosis studies with gene expression measurements. Statistics in Medicine. 30: 3361-71. PMID 22105693 DOI: 10.1002/Sim.4337 |
0.419 |
|
| 2011 |
Huang J, Ma S, Li H, Zhang CH. The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression. Annals of Statistics. 39: 2021-2046. PMID 22102764 DOI: 10.1214/11-Aos897 |
0.35 |
|
| 2011 |
Zhang Y, Dai Y, Zheng T, Ma S. Risk Factors of Non-Hodgkin Lymphoma. Expert Opinion On Medical Diagnostics. 5: 539-550. PMID 22059093 DOI: 10.1517/17530059.2011.618185 |
0.306 |
|
| 2011 |
Huang Y, Huang J, Shia BC, Ma S. Identification of cancer genomic markers via integrative sparse boosting. Biostatistics (Oxford, England). 13: 509-22. PMID 22045909 DOI: 10.1093/Biostatistics/Kxr033 |
0.401 |
|
| 2011 |
Li J, Ma S. Time-dependent ROC analysis under diverse censoring patterns. Statistics in Medicine. 30: 1266-77. PMID 21538452 DOI: 10.1002/Sim.4178 |
0.368 |
|
| 2011 |
Ma S, Huang J, Song X. Integrative analysis and variable selection with multiple high-dimensional data sets. Biostatistics (Oxford, England). 12: 763-75. PMID 21415015 DOI: 10.1093/Biostatistics/Kxr004 |
0.39 |
|
| 2011 |
Ma S, Dai Y. Principal component analysis based methods in bioinformatics studies Briefings in Bioinformatics. 12: 714-722. PMID 21242203 DOI: 10.1093/Bib/Bbq090 |
0.364 |
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| 2011 |
Ma S, Kosorok MR, Huang J, Dai Y. Incorporating higher-order representative features improves prediction in network-based cancer prognosis analysis. Bmc Medical Genomics. 4: 5. PMID 21226928 DOI: 10.1186/1755-8794-4-5 |
0.322 |
|
| 2011 |
Ma S, Song X. Ranking prognosis markers in cancer genomic studies. Briefings in Bioinformatics. 12: 33-40. PMID 21087949 DOI: 10.1093/Bib/Bbq069 |
0.411 |
|
| 2011 |
Shia BC, Zhu J, Fang K, Ma S. Fuzzy Canonical Discriminant Analysis: Theory and Practice Communications in Statistics - Simulation and Computation. 40: 1526-1539. DOI: 10.1080/03610918.2011.589337 |
0.309 |
|
| 2011 |
Ma S. Additive risk model for current status data with a cured subgroup Annals of the Institute of Statistical Mathematics. 63: 117-134. DOI: 10.1007/S10463-008-0212-2 |
0.36 |
|
| 2010 |
Song X, Ma S. Penalized variable selection with U-estimates. Journal of Nonparametric Statistics. 22: 499-515. PMID 21904440 DOI: 10.1080/10485250903348781 |
0.32 |
|
| 2010 |
Han X, Li Y, Huang J, Zhang Y, Holford T, Lan Q, Rothman N, Zheng T, Kosorok MR, Ma S. Identification of predictive pathways for non-hodgkin lymphoma prognosis. Cancer Informatics. 9: 281-92. PMID 21245948 DOI: 10.4137/Cin.S6315 |
0.317 |
|
| 2010 |
Ma S, Liu A, Carr J, Post W, Kronmal R. Statistical modeling of Agatston score in multi-ethnic study of atherosclerosis (MESA). Plos One. 5: e12036. PMID 20711503 DOI: 10.1371/Journal.Pone.0012036 |
0.337 |
|
| 2010 |
Ma S, Shi M, Li Y, Yi D, Shia BC. Incorporating gene co-expression network in identification of cancer prognosis markers. Bmc Bioinformatics. 11: 271. PMID 20487548 DOI: 10.1186/1471-2105-11-271 |
0.346 |
|
| 2010 |
Ma S, Huang J, Shi M, Li Y, Shia BC. Semiparametric prognosis models in genomic studies. Briefings in Bioinformatics. 11: 385-93. PMID 20123942 DOI: 10.1093/Bib/Bbp070 |
0.388 |
|
| 2010 |
Ma S, Kosorok MR. Detection of gene pathways with predictive power for breast cancer prognosis. Bmc Bioinformatics. 11: 1. PMID 20043860 DOI: 10.1186/1471-2105-11-1 |
0.338 |
|
| 2010 |
Ma S, Zhang Y, Huang J, Han X, Holford T, Lan Q, Rothman N, Boyle P, Zheng T. Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method. Bioinformatics (Oxford, England). 26: 15-21. PMID 19850755 DOI: 10.1093/Bioinformatics/Btp604 |
0.359 |
|
| 2010 |
Li J, Ma S. Interval‐censored data with repeated measurements and a cured subgroup Journal of the Royal Statistical Society Series C-Applied Statistics. 59: 693-705. DOI: 10.1111/J.1467-9876.2009.00702.X |
0.336 |
|
| 2009 |
Ma S, Huang J, Shen S. Identification of cancer-associated gene clusters and genes via clustering penalization. Statistics and Its Interface. 2: 1-11. PMID 20057914 DOI: 10.4310/Sii.2009.V2.N1.A1 |
0.331 |
|
| 2009 |
Ma S, Huang J, Moran MS. Identification of genes associated with multiple cancers via integrative analysis. Bmc Genomics. 10: 535. PMID 19919702 DOI: 10.1186/1471-2164-10-535 |
0.356 |
|
| 2009 |
Ma S, Kosorok MR. Identification of differential gene pathways with principal component analysis. Bioinformatics (Oxford, England). 25: 882-9. PMID 19223452 DOI: 10.1093/Bioinformatics/Btp085 |
0.309 |
|
| 2009 |
Ma S, Huang J. Regularized gene selection in cancer microarray meta-analysis. Bmc Bioinformatics. 10: 1. PMID 19118496 DOI: 10.1186/1471-2105-10-1 |
0.379 |
|
| 2008 |
Ma S, Huang J. Penalized feature selection and classification in bioinformatics. Briefings in Bioinformatics. 9: 392-403. PMID 18562478 DOI: 10.1093/Bib/Bbn027 |
0.307 |
|
| 2008 |
Song X, Ma S. Multiple augmentation for interval-censored data with measurement error. Statistics in Medicine. 27: 3178-90. PMID 18040984 DOI: 10.1002/Sim.3156 |
0.322 |
|
| 2007 |
Ma S, Huang J. Combining multiple markers for classification using ROC. Biometrics. 63: 751-7. PMID 17825007 DOI: 10.1111/J.1541-0420.2006.00731.X |
0.366 |
|
| 2007 |
Ma S, Huang J. Additive risk survival model with microarray data. Bmc Bioinformatics. 8: 192. PMID 17559667 DOI: 10.1186/1471-2105-8-192 |
0.36 |
|
| 2007 |
Ma S, Song X, Huang J. Supervised group Lasso with applications to microarray data analysis. Bmc Bioinformatics. 8: 60. PMID 17316436 DOI: 10.1186/1471-2105-8-60 |
0.324 |
|
| 2007 |
Leng C, Ma S. Path consistent model selection in additive risk model via Lasso. Statistics in Medicine. 26: 3753-70. PMID 17309043 DOI: 10.1002/Sim.2834 |
0.363 |
|
| 2006 |
Ma S, Huang J. Clustering threshold gradient descent regularization: with applications to microarray studies. Bioinformatics (Oxford, England). 23: 466-72. PMID 17182700 DOI: 10.1093/Bioinformatics/Btl632 |
0.314 |
|
| 2006 |
Ma S. Empirical study of supervised gene screening. Bmc Bioinformatics. 7: 537-537. PMID 17176468 DOI: 10.1186/1471-2105-7-537 |
0.31 |
|
| 2006 |
Huang J, Ma S, Xie H. Regularized estimation in the accelerated failure time model with high-dimensional covariates. Biometrics. 62: 813-20. PMID 16984324 DOI: 10.1111/J.1541-0420.2006.00562.X |
0.344 |
|
| 2006 |
Ma S, Kosorok MR, Huang J, Xie H, Manzella L, Soares MB. Robust semiparametric microarray normalization and significance analysis. Biometrics. 62: 555-61. PMID 16918920 DOI: 10.1111/J.1541-0420.2005.00452.X |
0.387 |
|
| 2006 |
Ma S, Song X, Huang J. Regularized binormal ROC method in disease classification using microarray data. Bmc Bioinformatics. 7: 253. PMID 16684357 DOI: 10.1186/1471-2105-7-253 |
0.351 |
|
| 2006 |
Song X, Ma S, Huang J, Zhou XH. A semiparametric approach for the nonparametric transformation survival model with multiple covariates. Biostatistics (Oxford, England). 8: 197-211. PMID 16670240 DOI: 10.1093/Biostatistics/Kxl001 |
0.338 |
|
| 2006 |
Ma S. Multiple augmentation with partial missing regressors. Biometrical Journal. 48: 83-92. PMID 16544814 DOI: 10.1002/Bimj.200510168 |
0.36 |
|
| 2006 |
Ma S, Kosorok MR, Fine JP. Additive risk models for survival data with high-dimensional covariates. Biometrics. 62: 202-10. PMID 16542247 DOI: 10.1111/J.1541-0420.2005.00405.X |
0.389 |
|
| 2006 |
Ma S, Kosorok MR. Adaptive penalized M-estimation with current status data Annals of the Institute of Statistical Mathematics. 58: 511-526. DOI: 10.1007/S10463-005-0026-4 |
0.338 |
|
| 2005 |
Ma S, Huang J. Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics (Oxford, England). 21: 4356-62. PMID 16234316 DOI: 10.1093/Bioinformatics/Bti724 |
0.358 |
|
| 2005 |
Ma S, Kosorok MR. Penalized log-likelihood estimation for partly linear transformation models with current status data The Annals of Statistics. 33: 2256-2290. DOI: 10.1214/009053605000000444 |
0.325 |
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