The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles ...
Abstract—In the estimation of gene networks from microarray gene expression data, we propose a statistical method for quantifi- cation of the hidden ...
The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles ...
Dive into the research topics of 'Identifying hidden confounders in gene networks by Bayesian networks'. Together they form a unique fingerprint. Sort by ...
Finally, we describe de-confounding, another novel method to identify network ... networks and dynamic Bayesian networks as models of gene regulatory networks.
Dec 27, 2022 · It aims to identify the overall causal network and quantify the effects of genes on the studied trait, including both direct and indirect ...
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach.
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Dec 20, 2019 · (D) Variable selection is used to determine a sparse Bayesian gene network ... hidden confounders. We also note that the deviation from uniform ...
Sep 29, 2021 · Author summary. Data analysis using Bayesian networks can help identify possible causal relationships between measured biological variables.
May 3, 2018 · Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our ...