BACKGROUND Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients. METHODS Health records from the Korea Na...

We have developed a novel class of antisense agents, RNA Lassos, which are capable of binding to and circularizing around complementary target RNAs. The RNA Lasso consists of a fixed sequence derived from the hairpin ribozyme and an antisense segment whose size and sequence can be varied to base pair with accessible sites in the target RNA. The ribozyme catalyzes self-processing of the 5'- and ...

In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and then pursuing model fitting is widely accepted. When the dimension is very high, finding an appropriate group structure is as difficult as the ori...

The Lasso is a popular and computationally efficient procedure for automatically performing both variable selection and coefficient shrinkage on linear regression models. One limitation of the Lasso is that the same tuning parameter is used for both variable selection and shrinkage. As a result, it typically ends up selecting a model with too many variables to prevent over shrinkage of the regr...

INTRODUCTION Electronic health data are potentially valuable resources for evaluating colonoscopy screening utilization and effectiveness. The ability to distinguish screening colonoscopies from exams performed for other purposes is critical for research that examines factors related to screening uptake and adherence, and the impact of screening on patient outcomes, but distinguishing between t...

Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive accuracy. The idea is to temporarily discard the variables that are conditionally independent w...

A variable selection method is required to deal with regression models many variables, and LASSO has been the most widely used methodology. However, as several authors have noted, sensitive outliers in data. For this reason, Robust-LASSO approach was introduced by applying some weighting schemes for each sample This research presented a comparative study of three Robust LASSO, namely Huber-LASS...

Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the shortest intervals based on the hard-thresholding estimator is larger than the l...

Nonparametric varying coefficient models are useful for studying the time-dependent effects of variables. Many procedures have been developed for estimation and variable selection in such models. However, existing work has focused on the case when the number of variables is fixed or smaller than the sample size. In this paper, we consider the problem of variable selection and estimation in vary...

In this work, we explore the problems of detecting the number of narrow-band, far-field targets and estimating their corresponding directions from single snapshot measurements. The principles of sparse signal recovery (SSR) are used for the single snapshot detection and estimation of multiple targets. In the SSR framework, the DoA estimation problem is grid based and can be posed as the lasso o...