This proposal develops novel statistical methods to select a small group of molecules from high-throughput data such as microarray, proteomic, and next generation sequence from biomedical research, especially for autism studies and brain tumors. It focuses on developing efficient methods and valid statistical tools for controlling false discovery rate an testing treatment effects on a group of molecules, for feature selection and model building in presence of errors-in-variables, endogeneity, and heavy-tail error distributions, and for predicting clinical outcomes and understanding molecular mechanisms. It develops semiparametric and nonparametric models to reduce modeling biases and to augment features. It furthers the developments on estimating large covariance matrices for understanding genetic network, statistical model building and inferences. It introduces multivariate independence screening and conditional independence screening techniques to reduce false negatives and false positives in variable screening, and develops computable and optimal penalized likelihood methods for an array of statistical models. The strength and weakness of each proposed method will be critically analyzed via theoretical investigations and simulation studies. Related software will be developed. Data sets from ongoing autism research, brain tumor, and other biomedical studies will be analyzed using the newly developed methods and the results will be further biologically confirmed and investigated. The research findings will have strong impact on statistical analysis of high throughput data for biomedical research and on understanding molecular mechanisms of autism, brain tumors, and other diseases.
This proposal develops novel statistical and bioinformatic tools for finding genes, proteins, and SNPs that are associated with clinical outcomes. Data sets from ongoing autism research, brain tumors and other biomedical studies will be critically analyzed using the newly developed statistical and bioinformatic methods, and the results will be further biologically confirmed and investigated. The research findings will have strong impact on understanding molecular mechanisms of autism, brain tumors, and other diseases and developing therapeutic targets.
|Wang, Weichen; Fan, Jianqing (2017) Asymptotics of empirical eigenstructure for high dimensional spiked covariance. Ann Stat 45:1342-1374|
|Cheng, Zhijian; Bosco, Dale B; Sun, Li et al. (2017) Neural Stem Cell-Conditioned Medium Suppresses Inflammation and Promotes Spinal Cord Injury Recovery. Cell Transplant 26:469-482|
|Fan, Jianqing; Han, Xu (2017) Estimation of the false discovery proportion with unknown dependence. J R Stat Soc Series B Stat Methodol 79:1143-1164|
|Fan, Jianqing; Li, Quefeng; Wang, Yuyan (2017) Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. J R Stat Soc Series B Stat Methodol 79:247-265|
|Fan, Jianqing; Liao, Yuan; Wang, Weichen (2016) PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS. Ann Stat 44:219-254|
|Rolfe, Alyssa J; Bosco, Dale B; Wang, Jingying et al. (2016) Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells. Cell Biosci 6:42|
|Dobriban, Edgar; Fan, Jianqing (2016) Regularity Properties for Sparse Regression. Commun Math Stat 4:1-19|
|Fan, Jianqing; Han, Fang; Liu, Han et al. (2016) Robust Inference of Risks of Large Portfolios. J Econom 194:298-308|
|Fan, Jianqing; Zhou, Wen-Xin (2016) Guarding against Spurious Discoveries in High Dimensions. J Mach Learn Res 17:|
|Jones, Zachary B; Ren, Yi (2016) Sphingolipids in spinal cord injury. Int J Physiol Pathophysiol Pharmacol 8:52-69|
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