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Updated: Jun 22, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Variable selection and dependency networks for genomewide data
1Department of Statistics and Department of Biobehavioral Nursing and Health Systems, University of Washington Seattle, WA 98195, USA. adobra@u.washington.edu
Abstract:
We describe a new stochastic search algorithm for linear regression models called the bounded mode stochastic search (BMSS). We make use of BMSS to perform variable selection and classification as well as to construct sparse dependency networks. Furthermore, we show how to determine genetic networks from genomewide data that involve any combination of continuous and discrete variables. We illustrate our methodology with several real-world data sets.
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