Exploring interactions in high-dimensional genomic data: an overview of Logic Regression, with applications
نویسندگان
چکیده
Logic Regression is an adaptive regression methodology mainly developed to explore highorder interactions in genomic data. Logic Regression is intended for situations where most of the covariates in the data to be analyzed are binary. The goal of Logic Regression is to find predictors that are Boolean (logical) combinations of the original predictors. In this article, we give an overview of the methodology and discuss some applications. We also describe the software for Logic Regression, which is available as an R and S-Plus package. r 2004 Elsevier Inc. All rights reserved. AMS 2000 subject classifications: 62J99
منابع مشابه
Extension of Logic regression to Longitudinal data: Transition Logic Regression
Logic regression is a generalized regression and classification method that is able to make Boolean combinations as new predictive variables from the original binary variables. Logic regression was introduced for case control or cohort study with independent observations. Although in various studies, correlated observations occur due to different reasons, logic regression have not been studi...
متن کاملRobust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data
Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...
متن کاملLogic regression and its application in predicting diseases
Regression is one of the most important statistical tools in data analysis and study of the relationship between predictive variables and the response variable. in most issues, regression models and decision tress only can show the main effects of predictor variables on the response and considering interactions between variables does not exceed of two way and ultimately three-way, due to co...
متن کاملAn Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کامل