ArticleBiostatistics (Oxford, England)2009
Variable selection and dependency networks for genomewide data.
Article in Biostatistics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.Bayesian analysis · 2020Article
- Markov Neighborhood Regression for High-Dimensional Inference.Journal of the American Statistical Association · 2020Article
- Exploring dependence between categorical variables: Benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms.Journal of statistical planning and inference · 2016Article
- Joint Bayesian variable and graph selection for regression models with network-structured predictors.Statistics in medicine · 2016Article
- Fatigue-related gene networks identified in CD14+ cells isolated from HIV-infected patients: part II: statistical analysis.Biological research for nursing · 2013Article
- Fatigue-related gene networks identified in CD(14)+ cells isolated from HIV-infected patients: part I: research findings.Biological research for nursing · 2013Article
- Construction of regulatory networks using expression time-series data of a genotyped population.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- A variable selection method for genome-wide association studies.Bioinformatics (Oxford, England) · 2011Article
Corrections and comments
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Authors and funding
1 author.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.