Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models

The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is big, the issue of variable selection arrives. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in ultra-high dimensional sparse varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance practical utility and the finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications

BATE Curve in Assessment of Clinical Utility of Predictive Biomarkers

In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect (BATE) curve, which can be used to represent the clinical utility of a predictive biomarker and select an optimal treatment for one particular patient. We then propose semi-parametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrate the application of the proposed method in a real-world data set

New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models

In biomedical research, one major objective is to identify risk factors and study their risk impacts, as this identification can help clinicians to both properly make a decision and increase efficiency of treatments and resource allocation. A two-step penalized-based procedure is proposed to select linear regression coefficients for linear components and to identify significant nonparametric varying-coefficient functions for semiparametric varying-coefficient partially linear Cox models. It is shown that the penalized-based resulting estimators of the linear regression coefficients are asymptotically normal and have oracle properties, and the resulting estimators of the varying-coefficient functions have optimal convergence rates. A simulation study and an empirical example are presented for illustration

Partial Derivative Estimation for Underlying Functional-Valued Process in a Unified Framework

We consider functional data analysis when the observations at each location are functional rather than scalar. When the dynamic of underlying functional-valued process at each location is of interest, it is desirable to recover partial derivatives of a sample function, especially from sparse and noise-contaminated measures. We propose a novel approach based on estimating derivatives of eigenfunctions of marginal kernels to obtain a representation for functional-valued process and its partial derivatives in a unified framework in which the number of locations and number of observations at each location for each individual can be any rate relative to the sample size. We derive almost sure rates of convergence for the procedures and further establish consistency results for recovered partial derivatives

RNA Interference against ATP as a Gene Therapy Approach for Prostate Cancer

Chemotherapeutic agents targeting energy metabolism have not achieved satisfactory results in different types of tumors. Herein, we developed an RNA interference (RNAi) method against adenosine triphosphate (ATP) by constructing an interfering plasmid-expressing ATP-binding RNA aptamer, which notably inhibited the growth of prostate cancer cells through diminishing the availability of cytoplasmic ATP and impairing the homeostasis of energy metabolism, and both glycolysis and oxidative phosphorylation were suppressed after RNAi treatment. Further identifying the mechanism underlying the effects of ATP aptamer, we surprisingly found that it markedly reduced the activity of membrane ionic channels and membrane potential which led to the dysfunction of mitochondria, such as the decrease of mitochondrial number, reduction in the respiration rate, and decline of mitochondrial membrane potential and ATP production. Meanwhile, the shortage of ATP impeded the formation of lamellipodia that are essential for the movement of cells, consequently resulting in a significant reduction of cell migration. Both the downregulation of the phosphorylation of AMP-activated protein kinase (AMPK) and endoplasmic reticulum kinase (ERK) and diminishing of lamellipodium formation led to cell apoptosis as well as the inhibition of angiogenesis and invasion. In conclusion, as the first RNAi modality targeting the blocking of ATP consumption, the present method can disturb the respiratory chain and ATP pool, which provides a novel regime for tumor therapies.