2012/06/13 by Se Young Kim, Eric P. Xing, Kim, Seyoung +1
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1206.3268
openalex publication_date 2012/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one typically encounters in problems such as whole genome association (WGA) mapping, remains an open problem in statistical learning. We propose a block-regularized regression model for sparse variable selection in a high-dimensional space where the covariates are linearly ordered, and are possibly subject to local statistical linkages (e.g., block structures) due to spacial or temporal proximity of the features. Our goal is to identify a small subset of relevant covariates that are not merely from random positions in the ordering, but grouped as contiguous blocks from large number of ordered covariates. Following a typical linear regression framework between the features and the response, our proposed model employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a 1st-order Markovian process along the feature sequence that "activates" the regression coefficients in a coupled fashion. We describe a sampling-based learning algorithm and demonstrate the performance of our method on simulated and biological data for marker identification under WGA.