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stat.AP2016
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
William Chad Young, Ka Yee Yeung, Adrian E. Raftery
Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variabili…
stat.AP2016
Model-based clustering with data correction for removing artifacts in gene expression data
William Chad Young, Ka Yee Yeung, Adrian E. Raftery
The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 color…